1. Importing the necessary libraries for analysis and visualizations¶

In [1]:
# importing the necessary libraries for analysis and visualizations
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns
import math
from datetime import datetime
import matplotlib.dates as mdates
from dateutil.relativedelta import relativedelta
from sklearn.preprocessing import MinMaxScaler
import copy

import warnings
warnings.filterwarnings("ignore")

2. Reading the data and preprocessing¶

In [2]:
# reading the data to a dataframe
df = pd.read_csv('all_ticks_wide.csv')

# converting to pd datetime
df['timestamp'] = pd.to_datetime(df['timestamp'])

# getting year, month, day as features
df['year'] = df['timestamp'].dt.year
df['month'] = df['timestamp'].dt.month
df['day'] = df['timestamp'].dt.day

df
Out[2]:
timestamp AEFES AKBNK AKSA AKSEN ALARK ALBRK ANACM ARCLK ASELS ... USAK VAKBN VESTL YATAS YKBNK YUNSA ZOREN year month day
0 2012-09-17 06:45:00+00:00 22.3978 5.2084 1.7102 3.87 1.4683 1.1356 1.0634 6.9909 2.9948 ... 1.0382 3.8620 1.90 0.4172 2.5438 2.2619 0.7789 2012 9 17
1 2012-09-17 07:00:00+00:00 22.3978 5.1938 1.7066 3.86 1.4574 1.1275 1.0634 6.9259 2.9948 ... 1.0382 3.8529 1.90 0.4229 2.5266 2.2462 0.7789 2012 9 17
2 2012-09-17 07:15:00+00:00 22.3978 5.2084 1.7102 NaN 1.4610 1.1356 1.0679 6.9909 2.9855 ... 1.0463 3.8436 1.91 0.4229 2.5266 2.2566 0.7789 2012 9 17
3 2012-09-17 07:30:00+00:00 22.3978 5.1938 1.7102 3.86 1.4537 1.1275 1.0679 6.9584 2.9855 ... 1.0382 3.8529 1.91 0.4286 2.5324 2.2619 0.7860 2012 9 17
4 2012-09-17 07:45:00+00:00 22.5649 5.2084 1.7102 3.87 1.4574 1.1356 1.0725 6.9909 2.9760 ... 1.0382 3.8620 1.90 0.4286 2.5324 2.2619 0.7789 2012 9 17
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
50007 2019-07-23 14:00:00+00:00 20.4800 7.7300 9.1400 2.47 3.2300 1.2100 2.8400 20.3000 NaN ... 1.0500 4.8600 9.98 5.3500 2.7500 4.2500 NaN 2019 7 23
50008 2019-07-23 14:15:00+00:00 20.5000 7.7200 9.1400 2.47 3.2200 1.2100 2.8400 20.3200 NaN ... 1.0500 4.8600 9.98 5.3400 2.7500 4.2400 NaN 2019 7 23
50009 2019-07-23 14:30:00+00:00 20.5000 7.7400 9.1300 2.46 3.2300 1.2100 2.8300 20.3400 NaN ... 1.0500 4.8600 9.96 5.3400 2.7600 4.2400 NaN 2019 7 23
50010 2019-07-23 14:45:00+00:00 20.4000 7.7000 9.1400 2.47 3.2400 1.2100 2.8200 20.3800 NaN ... 1.0400 4.8600 9.94 5.3400 2.7700 4.2400 NaN 2019 7 23
50011 2019-07-23 15:00:00+00:00 20.4600 7.7000 9.1400 2.47 3.2300 1.2000 2.8300 20.3200 NaN ... 1.0500 4.8500 9.93 5.3300 2.7700 4.2400 NaN 2019 7 23

50012 rows × 64 columns

In [3]:
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50012 entries, 0 to 50011
Data columns (total 64 columns):
 #   Column     Non-Null Count  Dtype              
---  ------     --------------  -----              
 0   timestamp  50012 non-null  datetime64[ns, UTC]
 1   AEFES      48131 non-null  float64            
 2   AKBNK      49209 non-null  float64            
 3   AKSA       48594 non-null  float64            
 4   AKSEN      48171 non-null  float64            
 5   ALARK      48335 non-null  float64            
 6   ALBRK      46862 non-null  float64            
 7   ANACM      48165 non-null  float64            
 8   ARCLK      49045 non-null  float64            
 9   ASELS      48803 non-null  float64            
 10  ASUZU      48433 non-null  float64            
 11  AYGAZ      48119 non-null  float64            
 12  BAGFS      48650 non-null  float64            
 13  BANVT      47951 non-null  float64            
 14  BRISA      48937 non-null  float64            
 15  CCOLA      48749 non-null  float64            
 16  CEMAS      46394 non-null  float64            
 17  ECILC      48492 non-null  float64            
 18  EREGL      49173 non-null  float64            
 19  FROTO      48995 non-null  float64            
 20  GARAN      49308 non-null  float64            
 21  GOODY      48961 non-null  float64            
 22  GUBRF      49057 non-null  float64            
 23  HALKB      49071 non-null  float64            
 24  ICBCT      44336 non-null  float64            
 25  ISCTR      49221 non-null  float64            
 26  ISDMR      12227 non-null  float64            
 27  ISFIN      42877 non-null  float64            
 28  ISYAT      43184 non-null  float64            
 29  KAREL      46032 non-null  float64            
 30  KARSN      48527 non-null  float64            
 31  KCHOL      49093 non-null  float64            
 32  KRDMB      47532 non-null  float64            
 33  KRDMD      49161 non-null  float64            
 34  MGROS      48903 non-null  float64            
 35  OTKAR      48785 non-null  float64            
 36  PARSN      45325 non-null  float64            
 37  PETKM      49184 non-null  float64            
 38  PGSUS      45221 non-null  float64            
 39  PRKME      48466 non-null  float64            
 40  SAHOL      49095 non-null  float64            
 41  SASA       47633 non-null  float64            
 42  SISE       49090 non-null  float64            
 43  SKBNK      47270 non-null  float64            
 44  SODA       48276 non-null  float64            
 45  TCELL      49143 non-null  float64            
 46  THYAO      49282 non-null  float64            
 47  TKFEN      48930 non-null  float64            
 48  TOASO      48946 non-null  float64            
 49  TRKCM      48886 non-null  float64            
 50  TSKB       48384 non-null  float64            
 51  TTKOM      49077 non-null  float64            
 52  TUKAS      45929 non-null  float64            
 53  TUPRS      49143 non-null  float64            
 54  USAK       47659 non-null  float64            
 55  VAKBN      49212 non-null  float64            
 56  VESTL      48781 non-null  float64            
 57  YATAS      46055 non-null  float64            
 58  YKBNK      49225 non-null  float64            
 59  YUNSA      45528 non-null  float64            
 60  ZOREN      48807 non-null  float64            
 61  year       50012 non-null  int64              
 62  month      50012 non-null  int64              
 63  day        50012 non-null  int64              
dtypes: datetime64[ns, UTC](1), float64(60), int64(3)
memory usage: 24.4 MB
In [4]:
# function to fill null values
# it takes the 
def fill_null_values_with_average(df, timestamp):
    filled_df = df.copy()
    other_columns = list(filled_df.columns.difference([timestamp]))
    for column in other_columns:
        # creating a mask for NaN values in the column
        null_mask = filled_df[column].isnull()
        # finding the first non-null value before each NaN
        before = filled_df[column].where(~null_mask).ffill()
        # finding the first non-null value after each NaN
        after = filled_df[column].where(~null_mask[::-1]).bfill()[::-1]
        # taking the average of them
        average = (before + after) / 2
        # filling the nan
        filled_df[column].fillna(average, inplace=True)
    return filled_df

# filling the nan
filled_df = fill_null_values_with_average(df, 'timestamp')
filled_df
Out[4]:
timestamp AEFES AKBNK AKSA AKSEN ALARK ALBRK ANACM ARCLK ASELS ... USAK VAKBN VESTL YATAS YKBNK YUNSA ZOREN year month day
0 2012-09-17 06:45:00+00:00 22.3978 5.2084 1.7102 3.87 1.4683 1.1356 1.0634 6.9909 2.9948 ... 1.0382 3.8620 1.90 0.4172 2.5438 2.2619 0.7789 2012 9 17
1 2012-09-17 07:00:00+00:00 22.3978 5.1938 1.7066 3.86 1.4574 1.1275 1.0634 6.9259 2.9948 ... 1.0382 3.8529 1.90 0.4229 2.5266 2.2462 0.7789 2012 9 17
2 2012-09-17 07:15:00+00:00 22.3978 5.2084 1.7102 3.86 1.4610 1.1356 1.0679 6.9909 2.9855 ... 1.0463 3.8436 1.91 0.4229 2.5266 2.2566 0.7789 2012 9 17
3 2012-09-17 07:30:00+00:00 22.3978 5.1938 1.7102 3.86 1.4537 1.1275 1.0679 6.9584 2.9855 ... 1.0382 3.8529 1.91 0.4286 2.5324 2.2619 0.7860 2012 9 17
4 2012-09-17 07:45:00+00:00 22.5649 5.2084 1.7102 3.87 1.4574 1.1356 1.0725 6.9909 2.9760 ... 1.0382 3.8620 1.90 0.4286 2.5324 2.2619 0.7789 2012 9 17
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
50007 2019-07-23 14:00:00+00:00 20.4800 7.7300 9.1400 2.47 3.2300 1.2100 2.8400 20.3000 NaN ... 1.0500 4.8600 9.98 5.3500 2.7500 4.2500 NaN 2019 7 23
50008 2019-07-23 14:15:00+00:00 20.5000 7.7200 9.1400 2.47 3.2200 1.2100 2.8400 20.3200 NaN ... 1.0500 4.8600 9.98 5.3400 2.7500 4.2400 NaN 2019 7 23
50009 2019-07-23 14:30:00+00:00 20.5000 7.7400 9.1300 2.46 3.2300 1.2100 2.8300 20.3400 NaN ... 1.0500 4.8600 9.96 5.3400 2.7600 4.2400 NaN 2019 7 23
50010 2019-07-23 14:45:00+00:00 20.4000 7.7000 9.1400 2.47 3.2400 1.2100 2.8200 20.3800 NaN ... 1.0400 4.8600 9.94 5.3400 2.7700 4.2400 NaN 2019 7 23
50011 2019-07-23 15:00:00+00:00 20.4600 7.7000 9.1400 2.47 3.2300 1.2000 2.8300 20.3200 NaN ... 1.0500 4.8500 9.93 5.3300 2.7700 4.2400 NaN 2019 7 23

50012 rows × 64 columns

In [5]:
# info about number of outliers
filled_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50012 entries, 0 to 50011
Data columns (total 64 columns):
 #   Column     Non-Null Count  Dtype              
---  ------     --------------  -----              
 0   timestamp  50012 non-null  datetime64[ns, UTC]
 1   AEFES      50012 non-null  float64            
 2   AKBNK      50012 non-null  float64            
 3   AKSA       50012 non-null  float64            
 4   AKSEN      50012 non-null  float64            
 5   ALARK      50012 non-null  float64            
 6   ALBRK      50012 non-null  float64            
 7   ANACM      50012 non-null  float64            
 8   ARCLK      50012 non-null  float64            
 9   ASELS      49982 non-null  float64            
 10  ASUZU      50012 non-null  float64            
 11  AYGAZ      50012 non-null  float64            
 12  BAGFS      50012 non-null  float64            
 13  BANVT      50012 non-null  float64            
 14  BRISA      50012 non-null  float64            
 15  CCOLA      50012 non-null  float64            
 16  CEMAS      50012 non-null  float64            
 17  ECILC      50012 non-null  float64            
 18  EREGL      50012 non-null  float64            
 19  FROTO      50012 non-null  float64            
 20  GARAN      50012 non-null  float64            
 21  GOODY      50012 non-null  float64            
 22  GUBRF      50012 non-null  float64            
 23  HALKB      50012 non-null  float64            
 24  ICBCT      50012 non-null  float64            
 25  ISCTR      50012 non-null  float64            
 26  ISDMR      26057 non-null  float64            
 27  ISFIN      50012 non-null  float64            
 28  ISYAT      50012 non-null  float64            
 29  KAREL      49980 non-null  float64            
 30  KARSN      49980 non-null  float64            
 31  KCHOL      50012 non-null  float64            
 32  KRDMB      50012 non-null  float64            
 33  KRDMD      50012 non-null  float64            
 34  MGROS      50012 non-null  float64            
 35  OTKAR      50012 non-null  float64            
 36  PARSN      50012 non-null  float64            
 37  PETKM      50012 non-null  float64            
 38  PGSUS      46015 non-null  float64            
 39  PRKME      50012 non-null  float64            
 40  SAHOL      50012 non-null  float64            
 41  SASA       49980 non-null  float64            
 42  SISE       49980 non-null  float64            
 43  SKBNK      50012 non-null  float64            
 44  SODA       50012 non-null  float64            
 45  TCELL      50012 non-null  float64            
 46  THYAO      50012 non-null  float64            
 47  TKFEN      50012 non-null  float64            
 48  TOASO      50012 non-null  float64            
 49  TRKCM      49980 non-null  float64            
 50  TSKB       50012 non-null  float64            
 51  TTKOM      50012 non-null  float64            
 52  TUKAS      50012 non-null  float64            
 53  TUPRS      50012 non-null  float64            
 54  USAK       50012 non-null  float64            
 55  VAKBN      50012 non-null  float64            
 56  VESTL      50012 non-null  float64            
 57  YATAS      50012 non-null  float64            
 58  YKBNK      50012 non-null  float64            
 59  YUNSA      50012 non-null  float64            
 60  ZOREN      49980 non-null  float64            
 61  year       50012 non-null  int64              
 62  month      50012 non-null  int64              
 63  day        50012 non-null  int64              
dtypes: datetime64[ns, UTC](1), float64(60), int64(3)
memory usage: 24.4 MB

3. Analysis¶

Now, we will select 6 stock indexes from 3 different sectors, preferably with non-null entries in the interval 15/01/2017 - 15/01/2019 . Let's consider

  • AKBNK
  • VAKBN
  • ARCLK
  • TUPRS
  • TCELL
  • THYAO

3.1. Control Charts¶

In [6]:
# copy df
df = filled_df.copy()
# start date
start = pd.to_datetime("2017-01-15 09:30:00+00:00")
# end data
end = pd.to_datetime("2019-01-15 09:30:00+00:00")
# filtering with time
ndf = df[(df['timestamp'] >= start) & (df['timestamp'] <= end)]
# filtering the necessary columns 
df = ndf[['timestamp', 'year', 'month', 'AKBNK', 'VAKBN', 'ARCLK', 'TUPRS', 'TCELL', 'THYAO']].copy()

# years and months as array
years = [2017, 2018, 2019]
months = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

# the selected stocks
indexes_selected = ['AKBNK', 'VAKBN', 'ARCLK', 'TUPRS', 'TCELL', 'THYAO']

outliers_akbnk = []
outliers_vakbn = []
outliers_arclk = []
outliers_tuprs = []
outliers_tcell = []
outliers_thyao = []

def get_list(index):
    if index == "AKBNK":
        return outliers_akbnk
    elif index == "VAKBN":
        return outliers_vakbn
    elif index == "ARCLK":
        return outliers_arclk
    elif index == "TUPRS":
        return outliers_tuprs
    elif index == "TCELL":
        return outliers_tcell
    else:
        return outliers_thyao

# given a stock in a specific month, constructs the 
# control charts for that month
def plot_control_chart(selected):
    for index in indexes_selected:
        # getting the prices for the stock
        prices = selected[index].to_list()
        # timestamps
        time = selected['timestamp'].to_list()
        # mean
        mu = np.mean(prices)
        # standard deviation
        sigma = np.std(prices)
        # UCL and LCL
        UCL = mu + 3 * sigma
        LCL = mu - 3 * sigma
        
        # create a list to store the outliers
        outliers = []
        # plot Mean, UCL, LCL
        plt.figure(figsize=(30, 15))
        plt.plot(time, prices, marker='o', label='Data')
        plt.axhline(mu, color='r', linestyle='--', label='Mean')
        plt.axhline(UCL, color='g', linestyle='--', label='UCL (3-sigma)')
        plt.axhline(LCL, color='g', linestyle='--', label='LCL (3-sigma)')
        
        # plot outliers as red dots
        for i, price in enumerate(prices):
            if price > UCL or price < LCL:
                plt.scatter(time[i], price, c='red', marker='o', s=200)
                outliers.append((time[i], price))
        # plot dates, x axis, y axis, title
        date_format = mdates.DateFormatter('%Y-%m-%d %H:%M:%S')
        plt.gca().xaxis.set_major_formatter(date_format)
        plt.gca().xaxis.set_major_locator(mdates.HourLocator(interval=12))
        plt.xlabel('Time', fontsize=26)
        plt.ylabel('Price', fontsize=26)
        plt.title(f'Control Chart for {index} with 3-sigma Limits\nYear: {selected["year"].iloc[0]}, Month: {selected["month"].iloc[0]}', fontsize=32, fontweight='bold')
        plt.legend(fontsize=25)
        plt.grid(True)
        plt.xticks(rotation=45, fontsize=18)
        plt.yticks(fontsize=20)
        plt.show()
        # print outliers
        if outliers:
            print(f'Outliers for {index}:')
            l = get_list(index)
            for outlier in outliers:
                l.append((outlier[0],outlier[1]))
                print(f'Timestamp: {outlier[0]}, Price: {outlier[1]}')

# group by year, month
grouped = df.groupby(['year', 'month'])

# apply the plot_control_chart function to each group
grouped.apply(plot_control_chart)
Outliers for AKBNK:
Timestamp: 2017-01-31 11:30:00+00:00, Price: 6.7643
Timestamp: 2017-01-31 14:15:00+00:00, Price: 6.7643
Timestamp: 2017-01-31 14:30:00+00:00, Price: 6.7803
Outliers for AKBNK:
Timestamp: 2017-02-01 08:15:00+00:00, Price: 6.6681
Timestamp: 2017-02-01 08:30:00+00:00, Price: 6.6521
Outliers for VAKBN:
Timestamp: 2017-02-01 06:45:00+00:00, Price: 4.8252
Timestamp: 2017-02-01 07:45:00+00:00, Price: 4.8153
Timestamp: 2017-02-01 08:15:00+00:00, Price: 4.8153
Timestamp: 2017-02-01 08:30:00+00:00, Price: 4.8055
Outliers for THYAO:
Timestamp: 2017-02-28 12:15:00+00:00, Price: 5.46
Timestamp: 2017-02-28 12:30:00+00:00, Price: 5.45
Timestamp: 2017-02-28 12:45:00+00:00, Price: 5.42
Timestamp: 2017-02-28 13:00:00+00:00, Price: 5.42
Timestamp: 2017-02-28 13:15:00+00:00, Price: 5.44
Timestamp: 2017-02-28 13:30:00+00:00, Price: 5.46
Timestamp: 2017-02-28 13:45:00+00:00, Price: 5.45
Timestamp: 2017-02-28 14:00:00+00:00, Price: 5.46
Timestamp: 2017-02-28 14:15:00+00:00, Price: 5.46
Timestamp: 2017-02-28 14:30:00+00:00, Price: 5.45
Timestamp: 2017-02-28 14:45:00+00:00, Price: 5.45
Timestamp: 2017-02-28 15:00:00+00:00, Price: 5.46
Outliers for AKBNK:
Timestamp: 2017-03-06 12:45:00+00:00, Price: 7.4375
Timestamp: 2017-03-06 13:00:00+00:00, Price: 7.4375
Timestamp: 2017-03-06 13:15:00+00:00, Price: 7.4375
Timestamp: 2017-03-06 13:30:00+00:00, Price: 7.4375
Timestamp: 2017-03-06 13:45:00+00:00, Price: 7.4294
Timestamp: 2017-03-06 14:00:00+00:00, Price: 7.4294
Timestamp: 2017-03-07 06:45:00+00:00, Price: 7.4535
Timestamp: 2017-03-07 07:00:00+00:00, Price: 7.4294
Timestamp: 2017-03-07 07:15:00+00:00, Price: 7.4615
Timestamp: 2017-03-07 07:30:00+00:00, Price: 7.4375
Timestamp: 2017-03-07 07:45:00+00:00, Price: 7.4455
Outliers for VAKBN:
Timestamp: 2017-03-06 12:15:00+00:00, Price: 5.4906
Timestamp: 2017-03-06 12:30:00+00:00, Price: 5.5005
Timestamp: 2017-03-06 12:45:00+00:00, Price: 5.5102
Timestamp: 2017-03-06 13:00:00+00:00, Price: 5.5102
Timestamp: 2017-03-06 13:15:00+00:00, Price: 5.52
Timestamp: 2017-03-06 13:30:00+00:00, Price: 5.5102
Timestamp: 2017-03-06 13:45:00+00:00, Price: 5.5102
Timestamp: 2017-03-06 15:00:00+00:00, Price: 5.4906
Timestamp: 2017-03-07 06:45:00+00:00, Price: 5.4906
Timestamp: 2017-03-07 07:15:00+00:00, Price: 5.5005
Outliers for TUPRS:
Timestamp: 2017-04-28 14:30:00+00:00, Price: 71.0298
Timestamp: 2017-04-28 14:45:00+00:00, Price: 71.0298
Timestamp: 2017-04-28 15:00:00+00:00, Price: 71.0298
Outliers for ARCLK:
Timestamp: 2017-06-01 06:45:00+00:00, Price: 23.7059
Timestamp: 2017-06-01 07:00:00+00:00, Price: 23.7831
Timestamp: 2017-06-01 07:15:00+00:00, Price: 23.7445
Timestamp: 2017-06-01 07:30:00+00:00, Price: 23.6866
Timestamp: 2017-06-01 07:45:00+00:00, Price: 23.7252
Timestamp: 2017-06-01 08:00:00+00:00, Price: 23.6866
Timestamp: 2017-06-01 08:15:00+00:00, Price: 23.6866
Timestamp: 2017-06-01 08:30:00+00:00, Price: 23.7252
Timestamp: 2017-06-01 08:45:00+00:00, Price: 23.6673
Timestamp: 2017-06-01 09:00:00+00:00, Price: 23.7252
Timestamp: 2017-06-01 09:15:00+00:00, Price: 23.7445
Timestamp: 2017-06-01 09:30:00+00:00, Price: 23.7638
Timestamp: 2017-06-01 09:45:00+00:00, Price: 23.8024
Timestamp: 2017-06-01 10:45:00+00:00, Price: 23.8024
Timestamp: 2017-06-01 11:00:00+00:00, Price: 23.7638
Timestamp: 2017-06-01 11:15:00+00:00, Price: 23.6866
Timestamp: 2017-06-01 11:30:00+00:00, Price: 23.8024
Timestamp: 2017-06-01 11:45:00+00:00, Price: 23.7638
Timestamp: 2017-06-01 12:00:00+00:00, Price: 23.7445
Timestamp: 2017-06-01 12:15:00+00:00, Price: 23.8024
Timestamp: 2017-06-01 12:30:00+00:00, Price: 23.7831
Timestamp: 2017-06-01 12:45:00+00:00, Price: 23.7831
Timestamp: 2017-06-01 13:00:00+00:00, Price: 23.7445
Timestamp: 2017-06-01 13:15:00+00:00, Price: 23.7252
Timestamp: 2017-06-01 13:30:00+00:00, Price: 23.6673
Timestamp: 2017-06-01 13:45:00+00:00, Price: 23.7059
Timestamp: 2017-06-01 14:00:00+00:00, Price: 23.648
Timestamp: 2017-06-01 14:15:00+00:00, Price: 23.6673
Timestamp: 2017-06-01 14:30:00+00:00, Price: 23.7252
Timestamp: 2017-06-01 14:45:00+00:00, Price: 23.841
Outliers for TUPRS:
Timestamp: 2017-08-01 09:00:00+00:00, Price: 84.2834
Timestamp: 2017-08-01 09:15:00+00:00, Price: 84.7596
Timestamp: 2017-08-01 09:30:00+00:00, Price: 84.7596
Timestamp: 2017-08-01 09:45:00+00:00, Price: 84.6008
Timestamp: 2017-08-01 10:45:00+00:00, Price: 84.6008
Timestamp: 2017-08-01 11:00:00+00:00, Price: 84.5215
Timestamp: 2017-08-01 12:15:00+00:00, Price: 84.6008
Timestamp: 2017-08-01 12:30:00+00:00, Price: 84.4421
Timestamp: 2017-08-01 12:45:00+00:00, Price: 84.204
Timestamp: 2017-08-01 13:00:00+00:00, Price: 84.3627
Timestamp: 2017-08-01 13:15:00+00:00, Price: 84.4421
Timestamp: 2017-08-01 13:30:00+00:00, Price: 84.2834
Timestamp: 2017-08-01 13:45:00+00:00, Price: 84.204
Timestamp: 2017-08-01 14:00:00+00:00, Price: 84.0453
Timestamp: 2017-08-01 14:15:00+00:00, Price: 84.0453
Timestamp: 2017-08-01 14:30:00+00:00, Price: 84.1246
Timestamp: 2017-08-02 06:45:00+00:00, Price: 84.7596
Outliers for AKBNK:
Timestamp: 2017-10-09 07:00:00+00:00, Price: 7.3946
Timestamp: 2017-10-09 07:30:00+00:00, Price: 7.4357
Timestamp: 2017-10-09 07:45:00+00:00, Price: 7.4439
Timestamp: 2017-10-09 08:00:00+00:00, Price: 7.4357
Timestamp: 2017-10-09 08:15:00+00:00, Price: 7.4192
Timestamp: 2017-10-09 08:30:00+00:00, Price: 7.4275
Timestamp: 2017-10-09 08:45:00+00:00, Price: 7.4192
Timestamp: 2017-10-09 09:00:00+00:00, Price: 7.4275
Timestamp: 2017-10-09 09:15:00+00:00, Price: 7.4357
Outliers for VAKBN:
Timestamp: 2017-10-09 07:30:00+00:00, Price: 5.8462
Timestamp: 2017-10-09 07:45:00+00:00, Price: 5.8462
Timestamp: 2017-10-09 08:15:00+00:00, Price: 5.8364
Timestamp: 2017-10-09 08:30:00+00:00, Price: 5.8364
Timestamp: 2017-10-09 08:45:00+00:00, Price: 5.8364
Timestamp: 2017-10-09 09:00:00+00:00, Price: 5.8364
Timestamp: 2017-10-09 09:15:00+00:00, Price: 5.8364
Timestamp: 2017-10-09 09:30:00+00:00, Price: 5.8462
Timestamp: 2017-10-09 10:45:00+00:00, Price: 5.8462
Timestamp: 2017-10-09 11:00:00+00:00, Price: 5.8364
Timestamp: 2017-10-20 07:45:00+00:00, Price: 6.5068
Timestamp: 2017-10-20 08:00:00+00:00, Price: 6.5265
Timestamp: 2017-10-20 08:15:00+00:00, Price: 6.5068
Timestamp: 2017-10-20 08:45:00+00:00, Price: 6.4969
Timestamp: 2017-10-20 09:15:00+00:00, Price: 6.4969
Outliers for TCELL:
Timestamp: 2017-11-02 06:45:00+00:00, Price: 13.6104
Timestamp: 2017-11-02 07:00:00+00:00, Price: 13.7186
Timestamp: 2017-11-02 07:15:00+00:00, Price: 13.6014
Timestamp: 2017-11-02 07:30:00+00:00, Price: 13.6014
Timestamp: 2017-11-02 07:45:00+00:00, Price: 13.6374
Timestamp: 2017-11-02 08:00:00+00:00, Price: 13.5563
Timestamp: 2017-11-02 10:45:00+00:00, Price: 13.5384
Timestamp: 2017-11-30 12:30:00+00:00, Price: 13.5294
Timestamp: 2017-11-30 13:15:00+00:00, Price: 13.5834
Timestamp: 2017-11-30 13:30:00+00:00, Price: 13.5384
Timestamp: 2017-11-30 13:45:00+00:00, Price: 13.5834
Timestamp: 2017-11-30 14:00:00+00:00, Price: 13.6824
Timestamp: 2017-11-30 14:15:00+00:00, Price: 13.7095
Timestamp: 2017-11-30 14:30:00+00:00, Price: 13.6284
Timestamp: 2017-11-30 14:45:00+00:00, Price: 13.6555
Timestamp: 2017-11-30 15:00:00+00:00, Price: 13.7005
Outliers for ARCLK:
Timestamp: 2017-12-01 06:45:00+00:00, Price: 19.1114
Timestamp: 2017-12-29 13:45:00+00:00, Price: 20.5013
Timestamp: 2017-12-29 14:15:00+00:00, Price: 20.5013
Timestamp: 2017-12-29 14:30:00+00:00, Price: 20.6751
Timestamp: 2017-12-29 14:45:00+00:00, Price: 20.7137
Timestamp: 2017-12-29 15:00:00+00:00, Price: 20.7716
Outliers for VAKBN:
Timestamp: 2018-02-01 07:30:00+00:00, Price: 7.5222
Timestamp: 2018-02-01 07:45:00+00:00, Price: 7.5321
Outliers for TCELL:
Timestamp: 2018-02-20 14:45:00+00:00, Price: 13.5483
Timestamp: 2018-02-20 15:00:00+00:00, Price: 13.5576
Outliers for VAKBN:
Timestamp: 2018-04-30 14:30:00+00:00, Price: 5.8857
Timestamp: 2018-04-30 14:45:00+00:00, Price: 5.8955
Timestamp: 2018-04-30 15:00:00+00:00, Price: 5.8857
Outliers for ARCLK:
Timestamp: 2018-05-02 07:15:00+00:00, Price: 18.67
Timestamp: 2018-05-02 07:30:00+00:00, Price: 18.7
Timestamp: 2018-05-02 07:45:00+00:00, Price: 18.57
Timestamp: 2018-05-02 08:00:00+00:00, Price: 18.65
Timestamp: 2018-05-02 08:15:00+00:00, Price: 18.57
Outliers for TUPRS:
Timestamp: 2018-05-10 07:45:00+00:00, Price: 83.5047
Timestamp: 2018-05-10 08:00:00+00:00, Price: 83.5047
Outliers for TCELL:
Timestamp: 2018-05-02 06:45:00+00:00, Price: 12.9823
Timestamp: 2018-05-02 07:00:00+00:00, Price: 12.8987
Timestamp: 2018-05-02 07:15:00+00:00, Price: 12.9173
Timestamp: 2018-05-02 08:15:00+00:00, Price: 12.908
Timestamp: 2018-05-31 14:15:00+00:00, Price: 11.1821
Timestamp: 2018-05-31 15:00:00+00:00, Price: 11.1356
Outliers for AKBNK:
Timestamp: 2018-06-01 06:45:00+00:00, Price: 6.8976
Outliers for THYAO:
Timestamp: 2018-08-17 07:15:00+00:00, Price: 14.65
Timestamp: 2018-08-17 07:30:00+00:00, Price: 14.35
Timestamp: 2018-08-17 07:45:00+00:00, Price: 14.48
Timestamp: 2018-08-17 08:15:00+00:00, Price: 14.67
Timestamp: 2018-08-17 08:30:00+00:00, Price: 14.62
Timestamp: 2018-08-17 08:45:00+00:00, Price: 14.57
Timestamp: 2018-08-17 09:00:00+00:00, Price: 14.6
Timestamp: 2018-08-17 09:15:00+00:00, Price: 14.52
Timestamp: 2018-08-17 09:30:00+00:00, Price: 14.58
Timestamp: 2018-08-17 09:45:00+00:00, Price: 14.57
Timestamp: 2018-08-17 10:00:00+00:00, Price: 14.58
Timestamp: 2018-08-17 10:45:00+00:00, Price: 14.59
Outliers for AKBNK:
Timestamp: 2018-11-01 06:45:00+00:00, Price: 5.6451
Timestamp: 2018-11-01 07:00:00+00:00, Price: 5.6365
Timestamp: 2018-11-01 07:15:00+00:00, Price: 5.5679
Timestamp: 2018-11-01 07:30:00+00:00, Price: 5.5507
Timestamp: 2018-11-01 07:45:00+00:00, Price: 5.5679
Timestamp: 2018-11-01 08:00:00+00:00, Price: 5.6193
Timestamp: 2018-11-01 08:15:00+00:00, Price: 5.6365
Timestamp: 2018-11-01 08:30:00+00:00, Price: 5.6536
Timestamp: 2018-11-01 08:45:00+00:00, Price: 5.7309
Timestamp: 2018-11-01 09:00:00+00:00, Price: 5.7223
Timestamp: 2018-11-01 09:15:00+00:00, Price: 5.7309
Timestamp: 2018-11-01 09:30:00+00:00, Price: 5.7394
Timestamp: 2018-11-01 11:15:00+00:00, Price: 5.7394
Timestamp: 2018-11-01 12:30:00+00:00, Price: 5.7309
Outliers for AKBNK:
Timestamp: 2018-12-03 06:45:00+00:00, Price: 6.6831
Timestamp: 2018-12-03 07:00:00+00:00, Price: 6.7003
Timestamp: 2018-12-03 07:15:00+00:00, Price: 6.6831
Timestamp: 2018-12-03 07:30:00+00:00, Price: 6.6917
Timestamp: 2018-12-03 07:45:00+00:00, Price: 6.6746
Timestamp: 2018-12-03 08:00:00+00:00, Price: 6.7089
Timestamp: 2018-12-03 08:15:00+00:00, Price: 6.7175
Timestamp: 2018-12-03 08:30:00+00:00, Price: 6.6746
Timestamp: 2018-12-03 08:45:00+00:00, Price: 6.6574
Timestamp: 2018-12-03 09:00:00+00:00, Price: 6.6402
Timestamp: 2018-12-03 09:15:00+00:00, Price: 6.6488
Timestamp: 2018-12-03 09:30:00+00:00, Price: 6.6574
Timestamp: 2018-12-03 09:45:00+00:00, Price: 6.6488
Timestamp: 2018-12-03 10:45:00+00:00, Price: 6.6317
Out[6]:

3.2. Box Plots¶

In [7]:
# box plot
def plot_box_plot(selected):
    # getting year and month info from df
    year = selected["year"].iloc[0]
    month = selected["month"].iloc[0]
    # looping over stocks
    for index in indexes_selected:
        # initializing the plot, title, x & y axis
        plt.figure(figsize=(30, 15))
        ax = sns.boxplot(data=selected, x='month', y=index)
        plt.title(str(index) + " " + f'Stock Price Boxplot for {year}/{month}', fontsize=30, fontweight='bold')
        plt.xlabel('Month', fontsize=25)
        plt.ylabel('Stock Price', fontsize=25)

        # identifying and marking both upper and lower outliers
        threshold = 1.5  # Adjust this threshold as needed
        upper_outliers = selected[selected[index] > (selected[index].quantile(0.75) + threshold * (selected[index].quantile(0.75) - selected[index].quantile(0.25)))]
        lower_outliers = selected[selected[index] < (selected[index].quantile(0.25) - threshold * (selected[index].quantile(0.75) - selected[index].quantile(0.25)))]
        
        # printing information about the outliers
        if not upper_outliers.empty:
            print('Upper Outliers for ' + str(index) + " in " + str(year) + "/" + str(month))
            for _, row in upper_outliers.iterrows():
                timestamp = row['timestamp']
                price = row[index]
                print(f'Timestamp: {timestamp}, Price: {price}')

        if not lower_outliers.empty:
            print('Lower Outliers for ' + str(index) + " in " + str(year) + "/" + str(month))
            for _, row in lower_outliers.iterrows():
                timestamp = row['timestamp']
                price = row[index]
                print(f'Timestamp: {timestamp}, Price: {price}')
                
        if lower_outliers.empty and upper_outliers.empty:
            print("NO OUTLIERS")

        plt.show()

# group by year, month
grouped = df.groupby(['year', 'month'])

# apply the plot_box_plot function to each group
grouped.apply(plot_box_plot)
Upper Outliers for AKBNK in 2017/1
Timestamp: 2017-01-30 10:45:00+00:00, Price: 6.5238
Timestamp: 2017-01-30 11:00:00+00:00, Price: 6.5478
Timestamp: 2017-01-30 11:15:00+00:00, Price: 6.572
Timestamp: 2017-01-30 11:30:00+00:00, Price: 6.5639
Timestamp: 2017-01-30 11:45:00+00:00, Price: 6.5559
Timestamp: 2017-01-30 12:00:00+00:00, Price: 6.572
Timestamp: 2017-01-30 12:15:00+00:00, Price: 6.5799
Timestamp: 2017-01-30 12:30:00+00:00, Price: 6.5799
Timestamp: 2017-01-30 12:45:00+00:00, Price: 6.572
Timestamp: 2017-01-30 13:00:00+00:00, Price: 6.596
Timestamp: 2017-01-30 13:15:00+00:00, Price: 6.612
Timestamp: 2017-01-30 13:30:00+00:00, Price: 6.604
Timestamp: 2017-01-30 13:45:00+00:00, Price: 6.6281
Timestamp: 2017-01-30 14:00:00+00:00, Price: 6.6441
Timestamp: 2017-01-30 14:15:00+00:00, Price: 6.6681
Timestamp: 2017-01-30 14:30:00+00:00, Price: 6.6842
Timestamp: 2017-01-30 14:45:00+00:00, Price: 6.6842
Timestamp: 2017-01-30 15:00:00+00:00, Price: 6.6842
Timestamp: 2017-01-31 06:45:00+00:00, Price: 6.6761
Timestamp: 2017-01-31 07:00:00+00:00, Price: 6.6921
Timestamp: 2017-01-31 07:15:00+00:00, Price: 6.6921
Timestamp: 2017-01-31 07:30:00+00:00, Price: 6.6921
Timestamp: 2017-01-31 07:45:00+00:00, Price: 6.6681
Timestamp: 2017-01-31 08:00:00+00:00, Price: 6.6681
Timestamp: 2017-01-31 08:15:00+00:00, Price: 6.6681
Timestamp: 2017-01-31 08:30:00+00:00, Price: 6.6681
Timestamp: 2017-01-31 08:45:00+00:00, Price: 6.6761
Timestamp: 2017-01-31 09:00:00+00:00, Price: 6.6761
Timestamp: 2017-01-31 09:15:00+00:00, Price: 6.6842
Timestamp: 2017-01-31 09:30:00+00:00, Price: 6.7082
Timestamp: 2017-01-31 09:45:00+00:00, Price: 6.7163
Timestamp: 2017-01-31 10:45:00+00:00, Price: 6.7002
Timestamp: 2017-01-31 11:00:00+00:00, Price: 6.7403
Timestamp: 2017-01-31 11:15:00+00:00, Price: 6.7563
Timestamp: 2017-01-31 11:30:00+00:00, Price: 6.7643
Timestamp: 2017-01-31 11:45:00+00:00, Price: 6.6921
Timestamp: 2017-01-31 12:00:00+00:00, Price: 6.6921
Timestamp: 2017-01-31 12:15:00+00:00, Price: 6.7082
Timestamp: 2017-01-31 12:30:00+00:00, Price: 6.6842
Timestamp: 2017-01-31 12:45:00+00:00, Price: 6.6842
Timestamp: 2017-01-31 13:00:00+00:00, Price: 6.6921
Timestamp: 2017-01-31 13:15:00+00:00, Price: 6.6761
Timestamp: 2017-01-31 13:30:00+00:00, Price: 6.6681
Timestamp: 2017-01-31 13:45:00+00:00, Price: 6.7322
Timestamp: 2017-01-31 14:00:00+00:00, Price: 6.7163
Timestamp: 2017-01-31 14:15:00+00:00, Price: 6.7643
Timestamp: 2017-01-31 14:30:00+00:00, Price: 6.7803
Timestamp: 2017-01-31 14:45:00+00:00, Price: 6.7563
Timestamp: 2017-01-31 15:00:00+00:00, Price: 6.7322
Upper Outliers for VAKBN in 2017/1
Timestamp: 2017-01-30 08:15:00+00:00, Price: 4.7469
Timestamp: 2017-01-30 08:30:00+00:00, Price: 4.7469
Timestamp: 2017-01-30 08:45:00+00:00, Price: 4.786
Timestamp: 2017-01-30 09:00:00+00:00, Price: 4.8153
Timestamp: 2017-01-30 09:15:00+00:00, Price: 4.8252
Timestamp: 2017-01-30 09:30:00+00:00, Price: 4.8153
Timestamp: 2017-01-30 09:45:00+00:00, Price: 4.8153
Timestamp: 2017-01-30 10:45:00+00:00, Price: 4.8252
Timestamp: 2017-01-30 11:00:00+00:00, Price: 4.8055
Timestamp: 2017-01-30 11:15:00+00:00, Price: 4.8252
Timestamp: 2017-01-30 11:30:00+00:00, Price: 4.8252
Timestamp: 2017-01-30 11:45:00+00:00, Price: 4.8252
Timestamp: 2017-01-30 12:00:00+00:00, Price: 4.8252
Timestamp: 2017-01-30 12:15:00+00:00, Price: 4.8349
Timestamp: 2017-01-30 12:30:00+00:00, Price: 4.8252
Timestamp: 2017-01-30 12:45:00+00:00, Price: 4.8153
Timestamp: 2017-01-30 13:00:00+00:00, Price: 4.8349
Timestamp: 2017-01-30 13:15:00+00:00, Price: 4.8545
Timestamp: 2017-01-30 13:30:00+00:00, Price: 4.8447
Timestamp: 2017-01-30 13:45:00+00:00, Price: 4.8447
Timestamp: 2017-01-30 14:00:00+00:00, Price: 4.8545
Timestamp: 2017-01-30 14:15:00+00:00, Price: 4.8545
Timestamp: 2017-01-30 14:30:00+00:00, Price: 4.8545
Timestamp: 2017-01-30 14:45:00+00:00, Price: 4.8643
Timestamp: 2017-01-30 15:00:00+00:00, Price: 4.8643
Timestamp: 2017-01-31 06:45:00+00:00, Price: 4.8741
Timestamp: 2017-01-31 07:00:00+00:00, Price: 4.8545
Timestamp: 2017-01-31 07:15:00+00:00, Price: 4.8349
Timestamp: 2017-01-31 07:30:00+00:00, Price: 4.8545
Timestamp: 2017-01-31 07:45:00+00:00, Price: 4.8252
Timestamp: 2017-01-31 08:00:00+00:00, Price: 4.7958
Timestamp: 2017-01-31 08:15:00+00:00, Price: 4.8153
Timestamp: 2017-01-31 08:30:00+00:00, Price: 4.8252
Timestamp: 2017-01-31 08:45:00+00:00, Price: 4.8643
Timestamp: 2017-01-31 09:00:00+00:00, Price: 4.8741
Timestamp: 2017-01-31 09:15:00+00:00, Price: 4.8741
Timestamp: 2017-01-31 09:30:00+00:00, Price: 4.8937
Timestamp: 2017-01-31 09:45:00+00:00, Price: 4.8838
Timestamp: 2017-01-31 10:45:00+00:00, Price: 4.8838
Timestamp: 2017-01-31 11:00:00+00:00, Price: 4.9034
Timestamp: 2017-01-31 11:15:00+00:00, Price: 4.8937
Timestamp: 2017-01-31 11:30:00+00:00, Price: 4.9034
Timestamp: 2017-01-31 11:45:00+00:00, Price: 4.8643
Timestamp: 2017-01-31 12:00:00+00:00, Price: 4.8545
Timestamp: 2017-01-31 12:15:00+00:00, Price: 4.8643
Timestamp: 2017-01-31 12:30:00+00:00, Price: 4.8252
Timestamp: 2017-01-31 12:45:00+00:00, Price: 4.8252
Timestamp: 2017-01-31 13:00:00+00:00, Price: 4.8349
Timestamp: 2017-01-31 13:15:00+00:00, Price: 4.8153
Timestamp: 2017-01-31 13:30:00+00:00, Price: 4.8252
Timestamp: 2017-01-31 13:45:00+00:00, Price: 4.8349
Timestamp: 2017-01-31 14:00:00+00:00, Price: 4.8349
Timestamp: 2017-01-31 14:15:00+00:00, Price: 4.8643
Timestamp: 2017-01-31 14:30:00+00:00, Price: 4.8349
Timestamp: 2017-01-31 14:45:00+00:00, Price: 4.8153
Timestamp: 2017-01-31 15:00:00+00:00, Price: 4.8055
Lower Outliers for VAKBN in 2017/1
Timestamp: 2017-01-16 06:45:00+00:00, Price: 4.4434
Timestamp: 2017-01-16 14:45:00+00:00, Price: 4.4337
Timestamp: 2017-01-16 15:00:00+00:00, Price: 4.4238
Timestamp: 2017-01-17 07:00:00+00:00, Price: 4.4337
Timestamp: 2017-01-17 08:45:00+00:00, Price: 4.4434
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Lower Outliers for AKBNK in 2017/2
Timestamp: 2017-02-01 06:45:00+00:00, Price: 6.7163
Timestamp: 2017-02-01 07:00:00+00:00, Price: 6.7403
Timestamp: 2017-02-01 07:15:00+00:00, Price: 6.7163
Timestamp: 2017-02-01 07:30:00+00:00, Price: 6.7242
Timestamp: 2017-02-01 07:45:00+00:00, Price: 6.6842
Timestamp: 2017-02-01 08:00:00+00:00, Price: 6.7082
Timestamp: 2017-02-01 08:15:00+00:00, Price: 6.6681
Timestamp: 2017-02-01 08:30:00+00:00, Price: 6.6521
Timestamp: 2017-02-01 08:45:00+00:00, Price: 6.6842
Timestamp: 2017-02-01 09:00:00+00:00, Price: 6.7082
Timestamp: 2017-02-01 09:15:00+00:00, Price: 6.7163
Timestamp: 2017-02-01 09:30:00+00:00, Price: 6.7082
Timestamp: 2017-02-01 09:45:00+00:00, Price: 6.7163
Timestamp: 2017-02-01 10:00:00+00:00, Price: 6.7163
Timestamp: 2017-02-01 10:45:00+00:00, Price: 6.7163
Timestamp: 2017-02-01 11:00:00+00:00, Price: 6.7163
Timestamp: 2017-02-01 11:15:00+00:00, Price: 6.7163
Timestamp: 2017-02-01 11:30:00+00:00, Price: 6.7242
Timestamp: 2017-02-01 11:45:00+00:00, Price: 6.7322
Timestamp: 2017-02-01 12:00:00+00:00, Price: 6.7242
Timestamp: 2017-02-01 12:15:00+00:00, Price: 6.7322
Timestamp: 2017-02-01 12:30:00+00:00, Price: 6.7322
Timestamp: 2017-02-01 12:45:00+00:00, Price: 6.7322
Timestamp: 2017-02-01 13:00:00+00:00, Price: 6.7322
Timestamp: 2017-02-01 13:15:00+00:00, Price: 6.7242
Timestamp: 2017-02-01 13:30:00+00:00, Price: 6.7242
Timestamp: 2017-02-01 13:45:00+00:00, Price: 6.7242
Timestamp: 2017-02-01 14:00:00+00:00, Price: 6.7483
Timestamp: 2017-02-01 14:30:00+00:00, Price: 6.7643
Timestamp: 2017-02-01 14:45:00+00:00, Price: 6.7643
Timestamp: 2017-02-01 15:00:00+00:00, Price: 6.7724
Lower Outliers for VAKBN in 2017/2
Timestamp: 2017-02-01 06:45:00+00:00, Price: 4.8252
Timestamp: 2017-02-01 07:00:00+00:00, Price: 4.8545
Timestamp: 2017-02-01 07:15:00+00:00, Price: 4.8349
Timestamp: 2017-02-01 07:30:00+00:00, Price: 4.8349
Timestamp: 2017-02-01 07:45:00+00:00, Price: 4.8153
Timestamp: 2017-02-01 08:00:00+00:00, Price: 4.8349
Timestamp: 2017-02-01 08:15:00+00:00, Price: 4.8153
Timestamp: 2017-02-01 08:30:00+00:00, Price: 4.8055
Timestamp: 2017-02-01 08:45:00+00:00, Price: 4.8349
Timestamp: 2017-02-01 09:00:00+00:00, Price: 4.8447
Timestamp: 2017-02-01 09:15:00+00:00, Price: 4.8447
Timestamp: 2017-02-01 09:30:00+00:00, Price: 4.8447
Upper Outliers for ARCLK in 2017/2
Timestamp: 2017-02-01 08:00:00+00:00, Price: 21.9405
Timestamp: 2017-02-01 08:15:00+00:00, Price: 21.9029
Timestamp: 2017-02-01 08:30:00+00:00, Price: 22.1282
Timestamp: 2017-02-01 08:45:00+00:00, Price: 22.0344
Timestamp: 2017-02-01 09:00:00+00:00, Price: 21.978
Timestamp: 2017-02-01 09:15:00+00:00, Price: 21.978
Timestamp: 2017-02-01 09:30:00+00:00, Price: 22.0344
Timestamp: 2017-02-01 09:45:00+00:00, Price: 22.1094
Timestamp: 2017-02-01 10:00:00+00:00, Price: 22.053150000000002
Timestamp: 2017-02-01 10:45:00+00:00, Price: 21.9969
Timestamp: 2017-02-01 11:00:00+00:00, Price: 22.0344
Timestamp: 2017-02-01 11:15:00+00:00, Price: 22.0719
Timestamp: 2017-02-01 11:30:00+00:00, Price: 22.0344
Timestamp: 2017-02-01 11:45:00+00:00, Price: 22.0907
Timestamp: 2017-02-01 12:00:00+00:00, Price: 22.1282
Timestamp: 2017-02-01 12:15:00+00:00, Price: 22.0531
Timestamp: 2017-02-01 12:30:00+00:00, Price: 22.0156
Timestamp: 2017-02-01 12:45:00+00:00, Price: 22.0156
Timestamp: 2017-02-01 13:00:00+00:00, Price: 22.0344
Timestamp: 2017-02-01 13:15:00+00:00, Price: 21.978
Timestamp: 2017-02-01 13:30:00+00:00, Price: 21.9969
Timestamp: 2017-02-01 13:45:00+00:00, Price: 22.0344
Timestamp: 2017-02-01 14:00:00+00:00, Price: 21.9593
Timestamp: 2017-02-01 14:15:00+00:00, Price: 21.978
Timestamp: 2017-02-01 14:30:00+00:00, Price: 21.978
Timestamp: 2017-02-01 14:45:00+00:00, Price: 22.0156
Timestamp: 2017-02-01 15:00:00+00:00, Price: 22.1094
Timestamp: 2017-02-02 06:45:00+00:00, Price: 22.1282
Timestamp: 2017-02-02 07:00:00+00:00, Price: 22.0531
Timestamp: 2017-02-02 07:15:00+00:00, Price: 22.3159
Timestamp: 2017-02-02 07:30:00+00:00, Price: 22.466
Timestamp: 2017-02-02 07:45:00+00:00, Price: 22.4472
Timestamp: 2017-02-02 08:00:00+00:00, Price: 22.0719
Timestamp: 2017-02-02 08:15:00+00:00, Price: 22.0907
Timestamp: 2017-02-02 08:30:00+00:00, Price: 22.0344
Timestamp: 2017-02-02 08:45:00+00:00, Price: 22.0344
Timestamp: 2017-02-02 09:00:00+00:00, Price: 22.0156
Timestamp: 2017-02-02 09:15:00+00:00, Price: 21.9593
Timestamp: 2017-02-02 09:30:00+00:00, Price: 21.9969
Timestamp: 2017-02-02 09:45:00+00:00, Price: 21.9593
Timestamp: 2017-02-02 10:45:00+00:00, Price: 21.9405
Timestamp: 2017-02-03 06:45:00+00:00, Price: 22.3347
Timestamp: 2017-02-03 07:00:00+00:00, Price: 22.3722
Timestamp: 2017-02-03 07:15:00+00:00, Price: 22.4285
Timestamp: 2017-02-03 07:30:00+00:00, Price: 22.3347
Timestamp: 2017-02-03 07:45:00+00:00, Price: 22.2783
Timestamp: 2017-02-03 08:00:00+00:00, Price: 22.2783
Timestamp: 2017-02-03 08:15:00+00:00, Price: 22.3159
Timestamp: 2017-02-03 08:30:00+00:00, Price: 22.3722
Timestamp: 2017-02-03 08:45:00+00:00, Price: 22.3159
Timestamp: 2017-02-03 09:00:00+00:00, Price: 22.2596
Timestamp: 2017-02-03 09:15:00+00:00, Price: 22.2783
Timestamp: 2017-02-03 09:30:00+00:00, Price: 22.2971
Timestamp: 2017-02-03 09:45:00+00:00, Price: 22.2596
Timestamp: 2017-02-03 10:45:00+00:00, Price: 22.3159
Timestamp: 2017-02-03 11:00:00+00:00, Price: 22.4098
Timestamp: 2017-02-03 11:15:00+00:00, Price: 22.3534
Timestamp: 2017-02-03 11:30:00+00:00, Price: 22.3347
Timestamp: 2017-02-03 11:45:00+00:00, Price: 22.3722
Timestamp: 2017-02-03 12:00:00+00:00, Price: 22.3159
Timestamp: 2017-02-03 12:15:00+00:00, Price: 22.3347
Timestamp: 2017-02-03 12:30:00+00:00, Price: 22.3159
Timestamp: 2017-02-03 12:45:00+00:00, Price: 22.3347
Timestamp: 2017-02-03 13:00:00+00:00, Price: 22.3722
Timestamp: 2017-02-03 13:15:00+00:00, Price: 22.2971
Timestamp: 2017-02-03 13:30:00+00:00, Price: 22.3159
Timestamp: 2017-02-03 13:45:00+00:00, Price: 22.3159
Timestamp: 2017-02-03 14:00:00+00:00, Price: 22.3347
Timestamp: 2017-02-03 14:15:00+00:00, Price: 22.3722
Timestamp: 2017-02-03 14:30:00+00:00, Price: 22.3347
Timestamp: 2017-02-03 14:45:00+00:00, Price: 22.2408
Timestamp: 2017-02-03 15:00:00+00:00, Price: 22.3347
Timestamp: 2017-02-06 06:45:00+00:00, Price: 22.5223
Timestamp: 2017-02-06 07:00:00+00:00, Price: 22.5599
Timestamp: 2017-02-06 07:15:00+00:00, Price: 22.5411
Timestamp: 2017-02-06 07:30:00+00:00, Price: 22.6161
Timestamp: 2017-02-06 07:45:00+00:00, Price: 22.4848
Timestamp: 2017-02-06 08:00:00+00:00, Price: 22.3159
Timestamp: 2017-02-06 08:15:00+00:00, Price: 22.2408
Timestamp: 2017-02-06 08:30:00+00:00, Price: 22.1845
Timestamp: 2017-02-06 08:45:00+00:00, Price: 22.1658
Timestamp: 2017-02-06 09:00:00+00:00, Price: 22.1845
Timestamp: 2017-02-06 09:15:00+00:00, Price: 22.2033
Timestamp: 2017-02-06 09:30:00+00:00, Price: 22.1469
Timestamp: 2017-02-06 09:45:00+00:00, Price: 22.1469
Timestamp: 2017-02-06 10:45:00+00:00, Price: 22.1094
Timestamp: 2017-02-06 11:00:00+00:00, Price: 22.1845
Timestamp: 2017-02-06 11:15:00+00:00, Price: 22.1469
Timestamp: 2017-02-06 11:30:00+00:00, Price: 22.1282
Timestamp: 2017-02-06 11:45:00+00:00, Price: 22.1658
Timestamp: 2017-02-06 12:00:00+00:00, Price: 22.222
Timestamp: 2017-02-06 12:15:00+00:00, Price: 22.2033
Timestamp: 2017-02-06 12:30:00+00:00, Price: 22.2033
Timestamp: 2017-02-06 12:45:00+00:00, Price: 22.2783
Timestamp: 2017-02-06 13:00:00+00:00, Price: 22.2971
Timestamp: 2017-02-06 13:15:00+00:00, Price: 22.2596
Timestamp: 2017-02-06 13:30:00+00:00, Price: 22.1469
Timestamp: 2017-02-06 13:45:00+00:00, Price: 22.0719
Timestamp: 2017-02-06 14:00:00+00:00, Price: 21.9969
Timestamp: 2017-02-06 14:15:00+00:00, Price: 22.0344
Timestamp: 2017-02-06 14:30:00+00:00, Price: 22.0719
Timestamp: 2017-02-06 14:45:00+00:00, Price: 22.0531
Timestamp: 2017-02-06 15:00:00+00:00, Price: 22.0531
Upper Outliers for TUPRS in 2017/2
Timestamp: 2017-02-14 06:45:00+00:00, Price: 64.8258
NO OUTLIERS
Lower Outliers for THYAO in 2017/2
Timestamp: 2017-02-01 08:30:00+00:00, Price: 5.51
Timestamp: 2017-02-28 07:45:00+00:00, Price: 5.52
Timestamp: 2017-02-28 12:00:00+00:00, Price: 5.48
Timestamp: 2017-02-28 12:15:00+00:00, Price: 5.46
Timestamp: 2017-02-28 12:30:00+00:00, Price: 5.45
Timestamp: 2017-02-28 12:45:00+00:00, Price: 5.42
Timestamp: 2017-02-28 13:00:00+00:00, Price: 5.42
Timestamp: 2017-02-28 13:15:00+00:00, Price: 5.44
Timestamp: 2017-02-28 13:30:00+00:00, Price: 5.46
Timestamp: 2017-02-28 13:45:00+00:00, Price: 5.45
Timestamp: 2017-02-28 14:00:00+00:00, Price: 5.46
Timestamp: 2017-02-28 14:15:00+00:00, Price: 5.46
Timestamp: 2017-02-28 14:30:00+00:00, Price: 5.45
Timestamp: 2017-02-28 14:45:00+00:00, Price: 5.45
Timestamp: 2017-02-28 15:00:00+00:00, Price: 5.46
Upper Outliers for AKBNK in 2017/3
Timestamp: 2017-03-06 08:15:00+00:00, Price: 7.3414
Timestamp: 2017-03-06 08:30:00+00:00, Price: 7.3894
Timestamp: 2017-03-06 08:45:00+00:00, Price: 7.3733
Timestamp: 2017-03-06 09:00:00+00:00, Price: 7.3894
Timestamp: 2017-03-06 09:15:00+00:00, Price: 7.3894
Timestamp: 2017-03-06 09:30:00+00:00, Price: 7.3814
Timestamp: 2017-03-06 09:45:00+00:00, Price: 7.3894
Timestamp: 2017-03-06 10:00:00+00:00, Price: 7.3934
Timestamp: 2017-03-06 10:45:00+00:00, Price: 7.3974
Timestamp: 2017-03-06 11:00:00+00:00, Price: 7.3894
Timestamp: 2017-03-06 11:15:00+00:00, Price: 7.4135
Timestamp: 2017-03-06 11:30:00+00:00, Price: 7.4054
Timestamp: 2017-03-06 11:45:00+00:00, Price: 7.3974
Timestamp: 2017-03-06 12:00:00+00:00, Price: 7.3974
Timestamp: 2017-03-06 12:15:00+00:00, Price: 7.4135
Timestamp: 2017-03-06 12:30:00+00:00, Price: 7.4135
Timestamp: 2017-03-06 12:45:00+00:00, Price: 7.4375
Timestamp: 2017-03-06 13:00:00+00:00, Price: 7.4375
Timestamp: 2017-03-06 13:15:00+00:00, Price: 7.4375
Timestamp: 2017-03-06 13:30:00+00:00, Price: 7.4375
Timestamp: 2017-03-06 13:45:00+00:00, Price: 7.4294
Timestamp: 2017-03-06 14:00:00+00:00, Price: 7.4294
Timestamp: 2017-03-06 14:15:00+00:00, Price: 7.4215
Timestamp: 2017-03-06 14:30:00+00:00, Price: 7.3974
Timestamp: 2017-03-06 14:45:00+00:00, Price: 7.4135
Timestamp: 2017-03-06 15:00:00+00:00, Price: 7.4215
Timestamp: 2017-03-07 06:45:00+00:00, Price: 7.4535
Timestamp: 2017-03-07 07:00:00+00:00, Price: 7.4294
Timestamp: 2017-03-07 07:15:00+00:00, Price: 7.4615
Timestamp: 2017-03-07 07:30:00+00:00, Price: 7.4375
Timestamp: 2017-03-07 07:45:00+00:00, Price: 7.4455
Timestamp: 2017-03-07 08:00:00+00:00, Price: 7.4135
Timestamp: 2017-03-07 08:15:00+00:00, Price: 7.3974
Timestamp: 2017-03-07 08:30:00+00:00, Price: 7.3974
Timestamp: 2017-03-07 08:45:00+00:00, Price: 7.3974
Timestamp: 2017-03-07 09:00:00+00:00, Price: 7.4054
Timestamp: 2017-03-07 09:15:00+00:00, Price: 7.4135
Timestamp: 2017-03-07 09:30:00+00:00, Price: 7.4135
Timestamp: 2017-03-07 09:45:00+00:00, Price: 7.4054
Timestamp: 2017-03-07 10:45:00+00:00, Price: 7.4135
Timestamp: 2017-03-07 11:00:00+00:00, Price: 7.4135
Timestamp: 2017-03-07 11:15:00+00:00, Price: 7.3974
Timestamp: 2017-03-07 11:30:00+00:00, Price: 7.3654
Timestamp: 2017-03-07 11:45:00+00:00, Price: 7.3414
Timestamp: 2017-03-07 12:00:00+00:00, Price: 7.3414
Timestamp: 2017-03-07 12:15:00+00:00, Price: 7.3414
Upper Outliers for VAKBN in 2017/3
Timestamp: 2017-03-06 09:15:00+00:00, Price: 5.4613
Timestamp: 2017-03-06 09:45:00+00:00, Price: 5.4613
Timestamp: 2017-03-06 10:00:00+00:00, Price: 5.4613
Timestamp: 2017-03-06 10:45:00+00:00, Price: 5.4613
Timestamp: 2017-03-06 11:00:00+00:00, Price: 5.4711
Timestamp: 2017-03-06 11:15:00+00:00, Price: 5.4809
Timestamp: 2017-03-06 11:30:00+00:00, Price: 5.4711
Timestamp: 2017-03-06 11:45:00+00:00, Price: 5.4613
Timestamp: 2017-03-06 12:00:00+00:00, Price: 5.4711
Timestamp: 2017-03-06 12:15:00+00:00, Price: 5.4906
Timestamp: 2017-03-06 12:30:00+00:00, Price: 5.5005
Timestamp: 2017-03-06 12:45:00+00:00, Price: 5.5102
Timestamp: 2017-03-06 13:00:00+00:00, Price: 5.5102
Timestamp: 2017-03-06 13:15:00+00:00, Price: 5.52
Timestamp: 2017-03-06 13:30:00+00:00, Price: 5.5102
Timestamp: 2017-03-06 13:45:00+00:00, Price: 5.5102
Timestamp: 2017-03-06 14:00:00+00:00, Price: 5.4711
Timestamp: 2017-03-06 14:30:00+00:00, Price: 5.4613
Timestamp: 2017-03-06 14:45:00+00:00, Price: 5.4711
Timestamp: 2017-03-06 15:00:00+00:00, Price: 5.4906
Timestamp: 2017-03-07 06:45:00+00:00, Price: 5.4906
Timestamp: 2017-03-07 07:00:00+00:00, Price: 5.4809
Timestamp: 2017-03-07 07:15:00+00:00, Price: 5.5005
Timestamp: 2017-03-07 07:30:00+00:00, Price: 5.4711
NO OUTLIERS
Upper Outliers for TUPRS in 2017/3
Timestamp: 2017-03-21 08:15:00+00:00, Price: 67.4099
Timestamp: 2017-03-21 08:30:00+00:00, Price: 67.7422
Timestamp: 2017-03-21 08:45:00+00:00, Price: 67.5945
Timestamp: 2017-03-21 09:00:00+00:00, Price: 67.3731
Timestamp: 2017-03-21 09:15:00+00:00, Price: 67.3731
Timestamp: 2017-03-21 09:45:00+00:00, Price: 67.3362
Timestamp: 2017-03-21 10:45:00+00:00, Price: 67.3362
Timestamp: 2017-03-21 15:00:00+00:00, Price: 67.3362
Lower Outliers for TUPRS in 2017/3
Timestamp: 2017-03-01 06:45:00+00:00, Price: 63.0538
Timestamp: 2017-03-01 07:00:00+00:00, Price: 63.0907
Timestamp: 2017-03-01 07:15:00+00:00, Price: 62.98
Timestamp: 2017-03-01 07:30:00+00:00, Price: 63.1276
Timestamp: 2017-03-01 07:45:00+00:00, Price: 63.1276
Timestamp: 2017-03-01 08:00:00+00:00, Price: 63.1276
Timestamp: 2017-03-01 08:15:00+00:00, Price: 63.2384
Timestamp: 2017-03-01 08:30:00+00:00, Price: 63.3491
Timestamp: 2017-03-01 08:45:00+00:00, Price: 63.2753
Timestamp: 2017-03-01 09:00:00+00:00, Price: 63.4599
Timestamp: 2017-03-01 09:15:00+00:00, Price: 63.423
Timestamp: 2017-03-01 09:30:00+00:00, Price: 63.4599
Timestamp: 2017-03-01 09:45:00+00:00, Price: 63.4599
Timestamp: 2017-03-01 10:45:00+00:00, Price: 63.3491
Timestamp: 2017-03-01 11:00:00+00:00, Price: 63.4968
Timestamp: 2017-03-01 11:15:00+00:00, Price: 63.4599
Timestamp: 2017-03-01 11:30:00+00:00, Price: 63.423
Timestamp: 2017-03-01 11:45:00+00:00, Price: 63.4968
Timestamp: 2017-03-01 12:00:00+00:00, Price: 63.5706
Timestamp: 2017-03-01 12:15:00+00:00, Price: 63.6075
Timestamp: 2017-03-01 12:30:00+00:00, Price: 63.5706
Timestamp: 2017-03-01 12:45:00+00:00, Price: 63.4599
Timestamp: 2017-03-01 13:00:00+00:00, Price: 63.6445
Timestamp: 2017-03-01 13:15:00+00:00, Price: 63.9029
Timestamp: 2017-03-01 13:30:00+00:00, Price: 63.7552
Timestamp: 2017-03-01 14:00:00+00:00, Price: 63.6814
Timestamp: 2017-03-01 14:15:00+00:00, Price: 63.7183
Timestamp: 2017-03-01 14:30:00+00:00, Price: 63.9029
Timestamp: 2017-03-01 14:45:00+00:00, Price: 63.9029
Timestamp: 2017-03-01 15:00:00+00:00, Price: 63.866
Timestamp: 2017-03-02 06:45:00+00:00, Price: 63.866
Timestamp: 2017-03-02 08:15:00+00:00, Price: 63.8291
Timestamp: 2017-03-02 08:30:00+00:00, Price: 63.8291
Timestamp: 2017-03-02 08:45:00+00:00, Price: 63.423
Timestamp: 2017-03-02 09:00:00+00:00, Price: 63.3861
Timestamp: 2017-03-02 09:15:00+00:00, Price: 63.5338
Timestamp: 2017-03-02 09:30:00+00:00, Price: 63.4599
Timestamp: 2017-03-02 09:45:00+00:00, Price: 63.3861
Timestamp: 2017-03-02 10:45:00+00:00, Price: 63.3861
Timestamp: 2017-03-02 11:00:00+00:00, Price: 63.2753
Timestamp: 2017-03-02 11:15:00+00:00, Price: 63.1645
Timestamp: 2017-03-02 11:30:00+00:00, Price: 63.2015
Timestamp: 2017-03-02 11:45:00+00:00, Price: 63.1645
Timestamp: 2017-03-02 12:00:00+00:00, Price: 63.1276
Timestamp: 2017-03-02 12:15:00+00:00, Price: 63.2753
Timestamp: 2017-03-02 12:30:00+00:00, Price: 63.1645
Timestamp: 2017-03-02 12:45:00+00:00, Price: 63.0907
Timestamp: 2017-03-02 13:00:00+00:00, Price: 63.0538
Timestamp: 2017-03-02 13:15:00+00:00, Price: 63.0169
Timestamp: 2017-03-02 13:30:00+00:00, Price: 63.0538
Timestamp: 2017-03-02 13:45:00+00:00, Price: 63.0538
Timestamp: 2017-03-02 14:00:00+00:00, Price: 63.1645
Timestamp: 2017-03-02 14:15:00+00:00, Price: 63.5338
Timestamp: 2017-03-02 14:30:00+00:00, Price: 63.4968
Timestamp: 2017-03-02 14:45:00+00:00, Price: 63.5706
Timestamp: 2017-03-02 15:00:00+00:00, Price: 63.7921
Timestamp: 2017-03-03 06:45:00+00:00, Price: 63.6814
Timestamp: 2017-03-03 07:00:00+00:00, Price: 63.1276
Timestamp: 2017-03-03 07:15:00+00:00, Price: 63.4968
Timestamp: 2017-03-03 07:30:00+00:00, Price: 63.3122
Timestamp: 2017-03-03 07:45:00+00:00, Price: 63.2384
Timestamp: 2017-03-03 08:00:00+00:00, Price: 63.3861
Timestamp: 2017-03-03 08:15:00+00:00, Price: 63.6445
Upper Outliers for TCELL in 2017/3
Timestamp: 2017-03-06 07:00:00+00:00, Price: 10.4275
Timestamp: 2017-03-06 07:15:00+00:00, Price: 10.4275
Timestamp: 2017-03-06 07:30:00+00:00, Price: 10.4694
Timestamp: 2017-03-06 07:45:00+00:00, Price: 10.4527
Timestamp: 2017-03-06 08:00:00+00:00, Price: 10.4778
Timestamp: 2017-03-06 08:15:00+00:00, Price: 10.4778
Timestamp: 2017-03-06 08:30:00+00:00, Price: 10.4443
Timestamp: 2017-03-06 08:45:00+00:00, Price: 10.4527
Timestamp: 2017-03-06 09:00:00+00:00, Price: 10.4527
Timestamp: 2017-03-06 09:15:00+00:00, Price: 10.4527
Timestamp: 2017-03-06 09:30:00+00:00, Price: 10.4527
Timestamp: 2017-03-06 09:45:00+00:00, Price: 10.4527
Timestamp: 2017-03-06 10:00:00+00:00, Price: 10.45685
Timestamp: 2017-03-06 10:45:00+00:00, Price: 10.461
Timestamp: 2017-03-06 11:00:00+00:00, Price: 10.4778
Timestamp: 2017-03-06 11:15:00+00:00, Price: 10.4443
Timestamp: 2017-03-06 11:30:00+00:00, Price: 10.4944
Timestamp: 2017-03-06 11:45:00+00:00, Price: 10.4778
Timestamp: 2017-03-06 12:00:00+00:00, Price: 10.4694
Timestamp: 2017-03-06 12:15:00+00:00, Price: 10.461
Timestamp: 2017-03-06 12:30:00+00:00, Price: 10.461
Timestamp: 2017-03-06 12:45:00+00:00, Price: 10.4527
Timestamp: 2017-03-06 13:00:00+00:00, Price: 10.461
Timestamp: 2017-03-06 13:15:00+00:00, Price: 10.4527
Timestamp: 2017-03-06 13:30:00+00:00, Price: 10.4527
Timestamp: 2017-03-06 13:45:00+00:00, Price: 10.4527
Timestamp: 2017-03-06 14:00:00+00:00, Price: 10.436
Timestamp: 2017-03-06 14:15:00+00:00, Price: 10.4275
Timestamp: 2017-03-06 14:30:00+00:00, Price: 10.4109
Timestamp: 2017-03-06 14:45:00+00:00, Price: 10.3858
Timestamp: 2017-03-06 15:00:00+00:00, Price: 10.4109
Timestamp: 2017-03-07 06:45:00+00:00, Price: 10.4443
Timestamp: 2017-03-07 07:00:00+00:00, Price: 10.4109
Timestamp: 2017-03-07 07:15:00+00:00, Price: 10.4109
Timestamp: 2017-03-07 07:30:00+00:00, Price: 10.3858
Timestamp: 2017-03-20 08:00:00+00:00, Price: 10.3775
Timestamp: 2017-03-20 08:15:00+00:00, Price: 10.3775
Timestamp: 2017-03-20 13:00:00+00:00, Price: 10.3942
Timestamp: 2017-03-20 13:15:00+00:00, Price: 10.4193
Timestamp: 2017-03-20 13:30:00+00:00, Price: 10.4109
Timestamp: 2017-03-20 13:45:00+00:00, Price: 10.3942
Timestamp: 2017-03-20 14:00:00+00:00, Price: 10.4024
Timestamp: 2017-03-20 14:15:00+00:00, Price: 10.4024
Timestamp: 2017-03-20 14:30:00+00:00, Price: 10.3858
Timestamp: 2017-03-20 14:45:00+00:00, Price: 10.3775
Timestamp: 2017-03-20 15:00:00+00:00, Price: 10.3858
Timestamp: 2017-03-21 06:45:00+00:00, Price: 10.4275
Timestamp: 2017-03-21 07:00:00+00:00, Price: 10.4275
Timestamp: 2017-03-21 07:15:00+00:00, Price: 10.3858
Timestamp: 2017-03-21 07:30:00+00:00, Price: 10.4109
Timestamp: 2017-03-21 07:45:00+00:00, Price: 10.3775
Timestamp: 2017-03-21 08:00:00+00:00, Price: 10.4275
Timestamp: 2017-03-21 08:15:00+00:00, Price: 10.4193
Timestamp: 2017-03-21 08:30:00+00:00, Price: 10.4024
Timestamp: 2017-03-21 08:45:00+00:00, Price: 10.3942
Timestamp: 2017-03-21 09:00:00+00:00, Price: 10.3775
Timestamp: 2017-03-21 09:15:00+00:00, Price: 10.3775
Lower Outliers for TCELL in 2017/3
Timestamp: 2017-03-13 07:30:00+00:00, Price: 9.9092
Timestamp: 2017-03-13 07:45:00+00:00, Price: 9.9175
Timestamp: 2017-03-13 08:00:00+00:00, Price: 9.9259
Timestamp: 2017-03-13 09:00:00+00:00, Price: 9.9342
Timestamp: 2017-03-13 09:15:00+00:00, Price: 9.9342
Timestamp: 2017-03-13 09:30:00+00:00, Price: 9.9342
Timestamp: 2017-03-13 09:45:00+00:00, Price: 9.9092
Timestamp: 2017-03-13 10:45:00+00:00, Price: 9.9175
Timestamp: 2017-03-13 11:00:00+00:00, Price: 9.9342
Timestamp: 2017-03-31 07:45:00+00:00, Price: 9.9342
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Upper Outliers for TUPRS in 2017/4
Timestamp: 2017-04-26 11:45:00+00:00, Price: 69.3235
Timestamp: 2017-04-26 12:45:00+00:00, Price: 69.3235
Timestamp: 2017-04-26 13:15:00+00:00, Price: 69.3632
Timestamp: 2017-04-26 13:45:00+00:00, Price: 69.3235
Timestamp: 2017-04-26 14:00:00+00:00, Price: 69.6409
Timestamp: 2017-04-26 14:15:00+00:00, Price: 69.6013
Timestamp: 2017-04-26 14:30:00+00:00, Price: 69.5616
Timestamp: 2017-04-26 14:45:00+00:00, Price: 69.7203
Timestamp: 2017-04-26 15:00:00+00:00, Price: 69.8393
Timestamp: 2017-04-27 06:45:00+00:00, Price: 69.7203
Timestamp: 2017-04-27 07:00:00+00:00, Price: 70.1964
Timestamp: 2017-04-27 07:15:00+00:00, Price: 70.0774
Timestamp: 2017-04-27 07:30:00+00:00, Price: 70.1171
Timestamp: 2017-04-27 07:45:00+00:00, Price: 70.1568
Timestamp: 2017-04-27 08:00:00+00:00, Price: 70.1171
Timestamp: 2017-04-27 08:15:00+00:00, Price: 70.1568
Timestamp: 2017-04-27 08:30:00+00:00, Price: 70.0377
Timestamp: 2017-04-27 08:45:00+00:00, Price: 70.0377
Timestamp: 2017-04-27 09:00:00+00:00, Price: 70.0377
Timestamp: 2017-04-27 09:15:00+00:00, Price: 70.0774
Timestamp: 2017-04-27 09:30:00+00:00, Price: 70.0774
Timestamp: 2017-04-27 09:45:00+00:00, Price: 70.0377
Timestamp: 2017-04-27 10:00:00+00:00, Price: 70.0377
Timestamp: 2017-04-27 10:45:00+00:00, Price: 70.0377
Timestamp: 2017-04-27 11:00:00+00:00, Price: 70.0377
Timestamp: 2017-04-27 11:15:00+00:00, Price: 70.1568
Timestamp: 2017-04-27 11:30:00+00:00, Price: 70.4742
Timestamp: 2017-04-27 11:45:00+00:00, Price: 70.3949
Timestamp: 2017-04-27 12:00:00+00:00, Price: 70.2758
Timestamp: 2017-04-27 12:15:00+00:00, Price: 70.3155
Timestamp: 2017-04-27 12:30:00+00:00, Price: 70.4345
Timestamp: 2017-04-27 12:45:00+00:00, Price: 70.4742
Timestamp: 2017-04-27 13:00:00+00:00, Price: 70.3552
Timestamp: 2017-04-27 13:15:00+00:00, Price: 70.3552
Timestamp: 2017-04-27 13:30:00+00:00, Price: 70.3155
Timestamp: 2017-04-27 13:45:00+00:00, Price: 70.4345
Timestamp: 2017-04-27 14:00:00+00:00, Price: 70.5536
Timestamp: 2017-04-27 14:15:00+00:00, Price: 70.633
Timestamp: 2017-04-27 14:30:00+00:00, Price: 70.4345
Timestamp: 2017-04-27 14:45:00+00:00, Price: 70.3949
Timestamp: 2017-04-27 15:00:00+00:00, Price: 70.3949
Timestamp: 2017-04-28 06:45:00+00:00, Price: 70.1964
Timestamp: 2017-04-28 07:00:00+00:00, Price: 70.2758
Timestamp: 2017-04-28 07:15:00+00:00, Price: 70.2758
Timestamp: 2017-04-28 07:30:00+00:00, Price: 70.1171
Timestamp: 2017-04-28 07:45:00+00:00, Price: 70.0377
Timestamp: 2017-04-28 08:00:00+00:00, Price: 70.0377
Timestamp: 2017-04-28 08:15:00+00:00, Price: 70.0774
Timestamp: 2017-04-28 08:30:00+00:00, Price: 70.0377
Timestamp: 2017-04-28 08:45:00+00:00, Price: 70.0377
Timestamp: 2017-04-28 09:00:00+00:00, Price: 69.9187
Timestamp: 2017-04-28 09:15:00+00:00, Price: 69.998
Timestamp: 2017-04-28 09:30:00+00:00, Price: 69.998
Timestamp: 2017-04-28 09:45:00+00:00, Price: 70.0774
Timestamp: 2017-04-28 10:00:00+00:00, Price: 70.0774
Timestamp: 2017-04-28 10:45:00+00:00, Price: 70.0774
Timestamp: 2017-04-28 11:00:00+00:00, Price: 70.1171
Timestamp: 2017-04-28 11:15:00+00:00, Price: 70.1171
Timestamp: 2017-04-28 11:30:00+00:00, Price: 70.1964
Timestamp: 2017-04-28 11:45:00+00:00, Price: 70.3155
Timestamp: 2017-04-28 12:00:00+00:00, Price: 70.2758
Timestamp: 2017-04-28 12:15:00+00:00, Price: 70.3155
Timestamp: 2017-04-28 12:30:00+00:00, Price: 70.2361
Timestamp: 2017-04-28 12:45:00+00:00, Price: 70.4742
Timestamp: 2017-04-28 13:00:00+00:00, Price: 70.4742
Timestamp: 2017-04-28 13:15:00+00:00, Price: 70.4345
Timestamp: 2017-04-28 13:30:00+00:00, Price: 70.4345
Timestamp: 2017-04-28 13:45:00+00:00, Price: 70.4742
Timestamp: 2017-04-28 14:00:00+00:00, Price: 70.4345
Timestamp: 2017-04-28 14:15:00+00:00, Price: 70.4345
Timestamp: 2017-04-28 14:30:00+00:00, Price: 71.0298
Timestamp: 2017-04-28 14:45:00+00:00, Price: 71.0298
Timestamp: 2017-04-28 15:00:00+00:00, Price: 71.0298
Lower Outliers for TUPRS in 2017/4
Timestamp: 2017-04-03 08:00:00+00:00, Price: 65.7488
Timestamp: 2017-04-03 08:45:00+00:00, Price: 65.6011
Timestamp: 2017-04-03 09:00:00+00:00, Price: 65.7856
Timestamp: 2017-04-03 11:15:00+00:00, Price: 65.7856
Timestamp: 2017-04-03 11:30:00+00:00, Price: 65.7488
Timestamp: 2017-04-03 12:00:00+00:00, Price: 65.6379
Timestamp: 2017-04-03 12:15:00+00:00, Price: 65.4535
Timestamp: 2017-04-03 12:30:00+00:00, Price: 65.5641
Timestamp: 2017-04-03 12:45:00+00:00, Price: 65.6011
Timestamp: 2017-04-03 13:00:00+00:00, Price: 65.5272
Timestamp: 2017-04-03 13:15:00+00:00, Price: 65.4903
Timestamp: 2017-04-03 13:30:00+00:00, Price: 65.5641
Timestamp: 2017-04-03 13:45:00+00:00, Price: 65.5272
Timestamp: 2017-04-03 14:00:00+00:00, Price: 65.6011
Timestamp: 2017-04-03 14:15:00+00:00, Price: 65.7488
Timestamp: 2017-04-04 15:00:00+00:00, Price: 65.7125
Timestamp: 2017-04-05 06:45:00+00:00, Price: 65.7125
NO OUTLIERS
Upper Outliers for THYAO in 2017/4
Timestamp: 2017-04-26 07:45:00+00:00, Price: 6.09
Timestamp: 2017-04-26 08:00:00+00:00, Price: 6.14
Timestamp: 2017-04-26 08:15:00+00:00, Price: 6.13
Timestamp: 2017-04-26 08:30:00+00:00, Price: 6.11
Timestamp: 2017-04-26 08:45:00+00:00, Price: 6.1
Timestamp: 2017-04-26 09:00:00+00:00, Price: 6.11
Timestamp: 2017-04-26 09:15:00+00:00, Price: 6.1
Timestamp: 2017-04-26 09:30:00+00:00, Price: 6.1
Timestamp: 2017-04-26 09:45:00+00:00, Price: 6.1
Timestamp: 2017-04-26 10:00:00+00:00, Price: 6.1
Timestamp: 2017-04-26 10:45:00+00:00, Price: 6.1
Timestamp: 2017-04-26 11:00:00+00:00, Price: 6.1
Timestamp: 2017-04-26 11:15:00+00:00, Price: 6.11
Timestamp: 2017-04-26 11:30:00+00:00, Price: 6.14
Timestamp: 2017-04-26 11:45:00+00:00, Price: 6.13
Timestamp: 2017-04-26 12:00:00+00:00, Price: 6.11
Timestamp: 2017-04-26 12:15:00+00:00, Price: 6.12
Timestamp: 2017-04-26 12:30:00+00:00, Price: 6.12
Timestamp: 2017-04-26 12:45:00+00:00, Price: 6.12
Timestamp: 2017-04-26 13:00:00+00:00, Price: 6.15
Timestamp: 2017-04-26 13:15:00+00:00, Price: 6.14
Timestamp: 2017-04-26 13:30:00+00:00, Price: 6.12
Timestamp: 2017-04-26 13:45:00+00:00, Price: 6.12
Timestamp: 2017-04-26 14:00:00+00:00, Price: 6.1
Timestamp: 2017-04-26 14:15:00+00:00, Price: 6.09
Timestamp: 2017-04-26 14:30:00+00:00, Price: 6.12
Timestamp: 2017-04-26 14:45:00+00:00, Price: 6.1
Timestamp: 2017-04-26 15:00:00+00:00, Price: 6.1
Timestamp: 2017-04-27 06:45:00+00:00, Price: 6.12
Timestamp: 2017-04-27 07:00:00+00:00, Price: 6.15
Timestamp: 2017-04-27 07:15:00+00:00, Price: 6.13
Timestamp: 2017-04-27 07:30:00+00:00, Price: 6.19
Timestamp: 2017-04-27 07:45:00+00:00, Price: 6.2
Timestamp: 2017-04-27 08:00:00+00:00, Price: 6.19
Timestamp: 2017-04-27 08:15:00+00:00, Price: 6.19
Timestamp: 2017-04-27 08:30:00+00:00, Price: 6.18
Timestamp: 2017-04-27 08:45:00+00:00, Price: 6.17
Timestamp: 2017-04-27 09:00:00+00:00, Price: 6.18
Timestamp: 2017-04-27 09:15:00+00:00, Price: 6.17
Timestamp: 2017-04-27 09:30:00+00:00, Price: 6.16
Timestamp: 2017-04-27 09:45:00+00:00, Price: 6.17
Timestamp: 2017-04-27 10:00:00+00:00, Price: 6.17
Timestamp: 2017-04-27 10:45:00+00:00, Price: 6.17
Timestamp: 2017-04-27 11:00:00+00:00, Price: 6.16
Timestamp: 2017-04-27 11:15:00+00:00, Price: 6.18
Timestamp: 2017-04-27 11:30:00+00:00, Price: 6.17
Timestamp: 2017-04-27 11:45:00+00:00, Price: 6.17
Timestamp: 2017-04-27 12:00:00+00:00, Price: 6.17
Timestamp: 2017-04-27 12:15:00+00:00, Price: 6.18
Timestamp: 2017-04-27 12:30:00+00:00, Price: 6.17
Timestamp: 2017-04-27 12:45:00+00:00, Price: 6.17
Timestamp: 2017-04-27 13:00:00+00:00, Price: 6.2
Timestamp: 2017-04-27 13:15:00+00:00, Price: 6.17
Timestamp: 2017-04-27 13:30:00+00:00, Price: 6.16
Timestamp: 2017-04-27 13:45:00+00:00, Price: 6.17
Timestamp: 2017-04-27 14:00:00+00:00, Price: 6.13
Timestamp: 2017-04-27 14:15:00+00:00, Price: 6.12
Timestamp: 2017-04-27 14:30:00+00:00, Price: 6.12
Timestamp: 2017-04-27 14:45:00+00:00, Price: 6.06
Timestamp: 2017-04-27 15:00:00+00:00, Price: 6.06
Timestamp: 2017-04-28 06:45:00+00:00, Price: 6.08
Timestamp: 2017-04-28 07:00:00+00:00, Price: 6.13
Timestamp: 2017-04-28 07:15:00+00:00, Price: 6.13
Timestamp: 2017-04-28 07:30:00+00:00, Price: 6.1
Timestamp: 2017-04-28 07:45:00+00:00, Price: 6.1
Timestamp: 2017-04-28 08:00:00+00:00, Price: 6.09
Timestamp: 2017-04-28 08:15:00+00:00, Price: 6.08
Timestamp: 2017-04-28 08:45:00+00:00, Price: 6.05
Timestamp: 2017-04-28 09:30:00+00:00, Price: 6.05
Timestamp: 2017-04-28 09:45:00+00:00, Price: 6.05
Timestamp: 2017-04-28 10:00:00+00:00, Price: 6.05
Timestamp: 2017-04-28 10:45:00+00:00, Price: 6.05
Timestamp: 2017-04-28 11:00:00+00:00, Price: 6.06
Timestamp: 2017-04-28 11:15:00+00:00, Price: 6.05
Timestamp: 2017-04-28 11:30:00+00:00, Price: 6.06
Timestamp: 2017-04-28 11:45:00+00:00, Price: 6.07
Timestamp: 2017-04-28 12:00:00+00:00, Price: 6.05
Timestamp: 2017-04-28 12:15:00+00:00, Price: 6.07
Timestamp: 2017-04-28 12:30:00+00:00, Price: 6.06
Timestamp: 2017-04-28 12:45:00+00:00, Price: 6.07
Timestamp: 2017-04-28 13:00:00+00:00, Price: 6.08
Timestamp: 2017-04-28 13:15:00+00:00, Price: 6.08
Timestamp: 2017-04-28 13:30:00+00:00, Price: 6.08
Timestamp: 2017-04-28 13:45:00+00:00, Price: 6.06
Timestamp: 2017-04-28 14:00:00+00:00, Price: 6.05
Timestamp: 2017-04-28 14:30:00+00:00, Price: 6.06
Timestamp: 2017-04-28 14:45:00+00:00, Price: 6.06
Timestamp: 2017-04-28 15:00:00+00:00, Price: 6.06
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Lower Outliers for TUPRS in 2017/5
Timestamp: 2017-05-04 15:00:00+00:00, Price: 69.2838
Upper Outliers for TCELL in 2017/5
Timestamp: 2017-05-03 11:15:00+00:00, Price: 10.4527
Timestamp: 2017-05-03 11:45:00+00:00, Price: 10.4527
Timestamp: 2017-05-03 12:15:00+00:00, Price: 10.4443
Timestamp: 2017-05-03 12:30:00+00:00, Price: 10.4443
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Upper Outliers for ARCLK in 2017/6
Timestamp: 2017-06-09 09:15:00+00:00, Price: 25.7522
Timestamp: 2017-06-09 09:30:00+00:00, Price: 25.7328
Timestamp: 2017-06-09 09:45:00+00:00, Price: 25.7328
Timestamp: 2017-06-09 10:45:00+00:00, Price: 25.7135
Timestamp: 2017-06-09 11:00:00+00:00, Price: 25.7715
Timestamp: 2017-06-09 11:15:00+00:00, Price: 25.7522
Timestamp: 2017-06-09 11:30:00+00:00, Price: 25.7328
Timestamp: 2017-06-09 11:45:00+00:00, Price: 25.7715
Timestamp: 2017-06-09 12:00:00+00:00, Price: 25.7715
Timestamp: 2017-06-09 12:15:00+00:00, Price: 25.7715
Timestamp: 2017-06-09 12:30:00+00:00, Price: 25.7715
Timestamp: 2017-06-09 12:45:00+00:00, Price: 25.7908
Timestamp: 2017-06-09 13:00:00+00:00, Price: 25.7328
Timestamp: 2017-06-09 13:15:00+00:00, Price: 25.7715
Timestamp: 2017-06-09 13:30:00+00:00, Price: 25.7715
Timestamp: 2017-06-09 13:45:00+00:00, Price: 25.7908
Timestamp: 2017-06-09 14:00:00+00:00, Price: 25.8101
Timestamp: 2017-06-09 14:15:00+00:00, Price: 25.8294
Timestamp: 2017-06-12 07:30:00+00:00, Price: 25.7522
Lower Outliers for ARCLK in 2017/6
Timestamp: 2017-06-01 06:45:00+00:00, Price: 23.7059
Timestamp: 2017-06-01 07:00:00+00:00, Price: 23.7831
Timestamp: 2017-06-01 07:15:00+00:00, Price: 23.7445
Timestamp: 2017-06-01 07:30:00+00:00, Price: 23.6866
Timestamp: 2017-06-01 07:45:00+00:00, Price: 23.7252
Timestamp: 2017-06-01 08:00:00+00:00, Price: 23.6866
Timestamp: 2017-06-01 08:15:00+00:00, Price: 23.6866
Timestamp: 2017-06-01 08:30:00+00:00, Price: 23.7252
Timestamp: 2017-06-01 08:45:00+00:00, Price: 23.6673
Timestamp: 2017-06-01 09:00:00+00:00, Price: 23.7252
Timestamp: 2017-06-01 09:15:00+00:00, Price: 23.7445
Timestamp: 2017-06-01 09:30:00+00:00, Price: 23.7638
Timestamp: 2017-06-01 09:45:00+00:00, Price: 23.8024
Timestamp: 2017-06-01 10:45:00+00:00, Price: 23.8024
Timestamp: 2017-06-01 11:00:00+00:00, Price: 23.7638
Timestamp: 2017-06-01 11:15:00+00:00, Price: 23.6866
Timestamp: 2017-06-01 11:30:00+00:00, Price: 23.8024
Timestamp: 2017-06-01 11:45:00+00:00, Price: 23.7638
Timestamp: 2017-06-01 12:00:00+00:00, Price: 23.7445
Timestamp: 2017-06-01 12:15:00+00:00, Price: 23.8024
Timestamp: 2017-06-01 12:30:00+00:00, Price: 23.7831
Timestamp: 2017-06-01 12:45:00+00:00, Price: 23.7831
Timestamp: 2017-06-01 13:00:00+00:00, Price: 23.7445
Timestamp: 2017-06-01 13:15:00+00:00, Price: 23.7252
Timestamp: 2017-06-01 13:30:00+00:00, Price: 23.6673
Timestamp: 2017-06-01 13:45:00+00:00, Price: 23.7059
Timestamp: 2017-06-01 14:00:00+00:00, Price: 23.648
Timestamp: 2017-06-01 14:15:00+00:00, Price: 23.6673
Timestamp: 2017-06-01 14:30:00+00:00, Price: 23.7252
Timestamp: 2017-06-01 14:45:00+00:00, Price: 23.841
Timestamp: 2017-06-01 15:00:00+00:00, Price: 23.9182
Timestamp: 2017-06-02 06:45:00+00:00, Price: 24.0147
Timestamp: 2017-06-02 07:00:00+00:00, Price: 24.4394
Timestamp: 2017-06-02 07:15:00+00:00, Price: 24.3236
Timestamp: 2017-06-02 07:30:00+00:00, Price: 24.3815
Timestamp: 2017-06-02 07:45:00+00:00, Price: 24.4201
Timestamp: 2017-06-02 08:00:00+00:00, Price: 24.3815
Timestamp: 2017-06-02 08:15:00+00:00, Price: 24.3622
Timestamp: 2017-06-02 08:30:00+00:00, Price: 24.3429
Timestamp: 2017-06-02 08:45:00+00:00, Price: 24.4394
Timestamp: 2017-06-02 09:00:00+00:00, Price: 24.4588
Lower Outliers for TUPRS in 2017/6
Timestamp: 2017-06-01 06:45:00+00:00, Price: 75.7915
Timestamp: 2017-06-01 07:00:00+00:00, Price: 75.7915
Timestamp: 2017-06-01 07:15:00+00:00, Price: 75.6328
Timestamp: 2017-06-01 07:30:00+00:00, Price: 75.7915
Timestamp: 2017-06-01 07:45:00+00:00, Price: 75.7519
Timestamp: 2017-06-01 08:00:00+00:00, Price: 75.7122
Timestamp: 2017-06-01 08:15:00+00:00, Price: 75.7915
Timestamp: 2017-06-01 08:30:00+00:00, Price: 75.7915
Timestamp: 2017-06-01 08:45:00+00:00, Price: 75.8312
Timestamp: 2017-06-01 09:00:00+00:00, Price: 75.8312
Timestamp: 2017-06-01 09:15:00+00:00, Price: 75.8312
Timestamp: 2017-06-01 09:30:00+00:00, Price: 75.8312
Timestamp: 2017-06-01 09:45:00+00:00, Price: 75.9106
Timestamp: 2017-06-01 10:45:00+00:00, Price: 75.7122
Timestamp: 2017-06-01 11:00:00+00:00, Price: 75.7915
Timestamp: 2017-06-01 11:15:00+00:00, Price: 75.7915
Timestamp: 2017-06-01 11:30:00+00:00, Price: 75.7915
Timestamp: 2017-06-01 11:45:00+00:00, Price: 75.8709
Timestamp: 2017-06-01 12:00:00+00:00, Price: 75.7915
Timestamp: 2017-06-01 12:15:00+00:00, Price: 76.0693
Timestamp: 2017-06-01 12:30:00+00:00, Price: 75.9503
Timestamp: 2017-06-01 12:45:00+00:00, Price: 76.1884
Timestamp: 2017-06-01 13:00:00+00:00, Price: 76.0693
Timestamp: 2017-06-01 13:15:00+00:00, Price: 76.1884
Timestamp: 2017-06-01 13:30:00+00:00, Price: 75.9503
Timestamp: 2017-06-01 13:45:00+00:00, Price: 76.109
Timestamp: 2017-06-01 14:00:00+00:00, Price: 75.9503
Timestamp: 2017-06-01 14:15:00+00:00, Price: 75.7915
Timestamp: 2017-06-01 14:30:00+00:00, Price: 75.7915
Timestamp: 2017-06-01 14:45:00+00:00, Price: 75.7915
Timestamp: 2017-06-01 15:00:00+00:00, Price: 75.9503
Timestamp: 2017-06-02 06:45:00+00:00, Price: 76.4265
Timestamp: 2017-06-02 07:00:00+00:00, Price: 76.5059
Timestamp: 2017-06-02 07:15:00+00:00, Price: 76.3868
Timestamp: 2017-06-02 07:30:00+00:00, Price: 76.5059
Timestamp: 2017-06-02 08:15:00+00:00, Price: 76.4265
Timestamp: 2017-06-02 08:30:00+00:00, Price: 76.4662
Timestamp: 2017-06-02 08:45:00+00:00, Price: 76.4265
Timestamp: 2017-06-02 09:00:00+00:00, Price: 76.4662
Timestamp: 2017-06-02 09:15:00+00:00, Price: 76.4662
Timestamp: 2017-06-02 09:30:00+00:00, Price: 76.3868
Timestamp: 2017-06-02 09:45:00+00:00, Price: 76.3868
Timestamp: 2017-06-02 10:00:00+00:00, Price: 76.40665
Timestamp: 2017-06-02 10:45:00+00:00, Price: 76.4265
Timestamp: 2017-06-02 11:00:00+00:00, Price: 76.5059
Timestamp: 2017-06-02 11:15:00+00:00, Price: 76.4265
Timestamp: 2017-06-02 11:30:00+00:00, Price: 76.4662
Timestamp: 2017-06-02 11:45:00+00:00, Price: 76.4662
Timestamp: 2017-06-02 12:00:00+00:00, Price: 76.5455
Timestamp: 2017-06-02 12:15:00+00:00, Price: 76.5059
Timestamp: 2017-06-02 12:30:00+00:00, Price: 76.4265
Timestamp: 2017-06-02 12:45:00+00:00, Price: 76.3471
Timestamp: 2017-06-02 13:00:00+00:00, Price: 76.4265
Timestamp: 2017-06-02 13:15:00+00:00, Price: 76.3868
Timestamp: 2017-06-02 13:30:00+00:00, Price: 76.4265
Timestamp: 2017-06-02 13:45:00+00:00, Price: 76.4662
Timestamp: 2017-06-02 14:00:00+00:00, Price: 76.4265
Timestamp: 2017-06-02 14:15:00+00:00, Price: 76.5059
Timestamp: 2017-06-02 14:45:00+00:00, Price: 76.3074
NO OUTLIERS
Upper Outliers for THYAO in 2017/6
Timestamp: 2017-06-23 08:00:00+00:00, Price: 7.76
Timestamp: 2017-06-23 08:45:00+00:00, Price: 7.77
Timestamp: 2017-06-23 15:00:00+00:00, Price: 7.77
Timestamp: 2017-06-28 07:00:00+00:00, Price: 7.83
Timestamp: 2017-06-28 07:15:00+00:00, Price: 7.9
Timestamp: 2017-06-28 07:30:00+00:00, Price: 7.93
Timestamp: 2017-06-28 07:45:00+00:00, Price: 7.92
Timestamp: 2017-06-28 08:00:00+00:00, Price: 7.87
Timestamp: 2017-06-28 08:15:00+00:00, Price: 7.88
Timestamp: 2017-06-28 08:30:00+00:00, Price: 7.88
Timestamp: 2017-06-28 08:45:00+00:00, Price: 7.86
Timestamp: 2017-06-28 09:00:00+00:00, Price: 7.88
Timestamp: 2017-06-28 09:15:00+00:00, Price: 7.88
Timestamp: 2017-06-28 09:30:00+00:00, Price: 7.88
Timestamp: 2017-06-28 09:45:00+00:00, Price: 7.89
Timestamp: 2017-06-28 10:00:00+00:00, Price: 7.89
Timestamp: 2017-06-28 10:45:00+00:00, Price: 7.89
Timestamp: 2017-06-28 11:00:00+00:00, Price: 7.89
Timestamp: 2017-06-28 11:15:00+00:00, Price: 7.9
Timestamp: 2017-06-28 11:30:00+00:00, Price: 7.91
Timestamp: 2017-06-28 11:45:00+00:00, Price: 7.93
Timestamp: 2017-06-28 12:00:00+00:00, Price: 7.92
Timestamp: 2017-06-28 12:15:00+00:00, Price: 7.93
Timestamp: 2017-06-28 12:30:00+00:00, Price: 7.95
Timestamp: 2017-06-28 12:45:00+00:00, Price: 7.94
Timestamp: 2017-06-28 13:00:00+00:00, Price: 7.96
Timestamp: 2017-06-28 13:15:00+00:00, Price: 7.95
Timestamp: 2017-06-28 13:30:00+00:00, Price: 7.94
Timestamp: 2017-06-28 13:45:00+00:00, Price: 7.94
Timestamp: 2017-06-28 14:00:00+00:00, Price: 7.95
Timestamp: 2017-06-28 14:15:00+00:00, Price: 7.99
Timestamp: 2017-06-28 14:30:00+00:00, Price: 8.01
Timestamp: 2017-06-28 14:45:00+00:00, Price: 7.99
Timestamp: 2017-06-28 15:00:00+00:00, Price: 7.99
Timestamp: 2017-06-29 06:45:00+00:00, Price: 8.02
Timestamp: 2017-06-29 07:00:00+00:00, Price: 8.09
Timestamp: 2017-06-29 07:15:00+00:00, Price: 8.09
Timestamp: 2017-06-29 07:30:00+00:00, Price: 8.08
Timestamp: 2017-06-29 07:45:00+00:00, Price: 8.05
Timestamp: 2017-06-29 08:00:00+00:00, Price: 8.06
Timestamp: 2017-06-29 08:15:00+00:00, Price: 8.06
Timestamp: 2017-06-29 08:30:00+00:00, Price: 8.04
Timestamp: 2017-06-29 08:45:00+00:00, Price: 8.03
Timestamp: 2017-06-29 09:00:00+00:00, Price: 8.03
Timestamp: 2017-06-29 09:15:00+00:00, Price: 8.01
Timestamp: 2017-06-29 09:30:00+00:00, Price: 7.97
Timestamp: 2017-06-29 09:45:00+00:00, Price: 7.99
Timestamp: 2017-06-29 10:45:00+00:00, Price: 7.99
Timestamp: 2017-06-29 11:00:00+00:00, Price: 7.98
Timestamp: 2017-06-29 11:15:00+00:00, Price: 7.95
Timestamp: 2017-06-29 11:30:00+00:00, Price: 7.95
Timestamp: 2017-06-29 11:45:00+00:00, Price: 7.89
Timestamp: 2017-06-29 12:00:00+00:00, Price: 7.88
Timestamp: 2017-06-29 12:15:00+00:00, Price: 7.88
Timestamp: 2017-06-29 12:30:00+00:00, Price: 7.84
Timestamp: 2017-06-29 12:45:00+00:00, Price: 7.86
Timestamp: 2017-06-29 13:00:00+00:00, Price: 7.86
Timestamp: 2017-06-29 13:15:00+00:00, Price: 7.83
Timestamp: 2017-06-29 13:30:00+00:00, Price: 7.82
Timestamp: 2017-06-29 13:45:00+00:00, Price: 7.79
Timestamp: 2017-06-29 14:00:00+00:00, Price: 7.8
Timestamp: 2017-06-29 14:15:00+00:00, Price: 7.82
Timestamp: 2017-06-29 14:30:00+00:00, Price: 7.83
Timestamp: 2017-06-29 14:45:00+00:00, Price: 7.83
Timestamp: 2017-06-29 15:00:00+00:00, Price: 7.81
Timestamp: 2017-06-30 06:45:00+00:00, Price: 7.84
Timestamp: 2017-06-30 07:00:00+00:00, Price: 7.88
Timestamp: 2017-06-30 07:15:00+00:00, Price: 7.88
Timestamp: 2017-06-30 07:30:00+00:00, Price: 7.92
Timestamp: 2017-06-30 07:45:00+00:00, Price: 7.94
Timestamp: 2017-06-30 08:00:00+00:00, Price: 7.97
Timestamp: 2017-06-30 08:15:00+00:00, Price: 7.96
Timestamp: 2017-06-30 08:30:00+00:00, Price: 7.91
Timestamp: 2017-06-30 08:45:00+00:00, Price: 7.93
Timestamp: 2017-06-30 09:00:00+00:00, Price: 7.94
Timestamp: 2017-06-30 09:15:00+00:00, Price: 7.93
Timestamp: 2017-06-30 09:30:00+00:00, Price: 7.93
Timestamp: 2017-06-30 09:45:00+00:00, Price: 7.94
Timestamp: 2017-06-30 10:45:00+00:00, Price: 7.94
Timestamp: 2017-06-30 11:00:00+00:00, Price: 7.97
Timestamp: 2017-06-30 11:15:00+00:00, Price: 7.96
Timestamp: 2017-06-30 11:30:00+00:00, Price: 7.96
Timestamp: 2017-06-30 11:45:00+00:00, Price: 7.97
Timestamp: 2017-06-30 12:00:00+00:00, Price: 7.94
Timestamp: 2017-06-30 12:15:00+00:00, Price: 7.95
Timestamp: 2017-06-30 12:30:00+00:00, Price: 7.92
Timestamp: 2017-06-30 12:45:00+00:00, Price: 7.92
Timestamp: 2017-06-30 13:00:00+00:00, Price: 7.94
Timestamp: 2017-06-30 13:15:00+00:00, Price: 7.93
Timestamp: 2017-06-30 13:30:00+00:00, Price: 7.93
Timestamp: 2017-06-30 13:45:00+00:00, Price: 7.94
Timestamp: 2017-06-30 14:00:00+00:00, Price: 7.98
Timestamp: 2017-06-30 14:15:00+00:00, Price: 7.99
Timestamp: 2017-06-30 14:30:00+00:00, Price: 8.0
Timestamp: 2017-06-30 14:45:00+00:00, Price: 8.05
Timestamp: 2017-06-30 15:00:00+00:00, Price: 8.05
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Upper Outliers for TCELL in 2017/7
Timestamp: 2017-07-28 12:15:00+00:00, Price: 11.4711
Timestamp: 2017-07-28 12:30:00+00:00, Price: 11.4711
Timestamp: 2017-07-28 12:45:00+00:00, Price: 11.4711
Lower Outliers for THYAO in 2017/7
Timestamp: 2017-07-03 06:45:00+00:00, Price: 8.1
Timestamp: 2017-07-03 07:00:00+00:00, Price: 8.09
Timestamp: 2017-07-03 07:15:00+00:00, Price: 8.08
Timestamp: 2017-07-03 07:30:00+00:00, Price: 8.09
Timestamp: 2017-07-03 07:45:00+00:00, Price: 8.1
Timestamp: 2017-07-03 08:00:00+00:00, Price: 8.11
Timestamp: 2017-07-03 08:15:00+00:00, Price: 8.12
Timestamp: 2017-07-03 08:30:00+00:00, Price: 8.11
Timestamp: 2017-07-03 08:45:00+00:00, Price: 8.16
Timestamp: 2017-07-03 09:00:00+00:00, Price: 8.13
Timestamp: 2017-07-03 09:15:00+00:00, Price: 8.11
Timestamp: 2017-07-03 09:30:00+00:00, Price: 8.1
Timestamp: 2017-07-03 09:45:00+00:00, Price: 8.1
Timestamp: 2017-07-03 10:45:00+00:00, Price: 8.1
Timestamp: 2017-07-03 11:00:00+00:00, Price: 8.12
Timestamp: 2017-07-03 11:15:00+00:00, Price: 8.1
Timestamp: 2017-07-03 11:30:00+00:00, Price: 8.1
Timestamp: 2017-07-03 11:45:00+00:00, Price: 8.1
Timestamp: 2017-07-03 12:00:00+00:00, Price: 8.09
Timestamp: 2017-07-03 12:15:00+00:00, Price: 8.09
Timestamp: 2017-07-03 12:30:00+00:00, Price: 8.09
Timestamp: 2017-07-03 12:45:00+00:00, Price: 8.1
Timestamp: 2017-07-03 13:00:00+00:00, Price: 8.1
Timestamp: 2017-07-03 13:15:00+00:00, Price: 8.07
Timestamp: 2017-07-03 13:30:00+00:00, Price: 8.08
Timestamp: 2017-07-03 13:45:00+00:00, Price: 8.09
Timestamp: 2017-07-03 14:00:00+00:00, Price: 8.09
Timestamp: 2017-07-03 14:15:00+00:00, Price: 8.08
Timestamp: 2017-07-03 14:30:00+00:00, Price: 8.07
Timestamp: 2017-07-03 14:45:00+00:00, Price: 8.07
Timestamp: 2017-07-03 15:00:00+00:00, Price: 8.06
Timestamp: 2017-07-04 06:45:00+00:00, Price: 8.17
Timestamp: 2017-07-04 07:00:00+00:00, Price: 8.19
Timestamp: 2017-07-04 08:00:00+00:00, Price: 8.22
Timestamp: 2017-07-04 08:45:00+00:00, Price: 8.21
Timestamp: 2017-07-04 09:00:00+00:00, Price: 8.22
Timestamp: 2017-07-06 12:00:00+00:00, Price: 8.21
Timestamp: 2017-07-06 12:15:00+00:00, Price: 8.21
Timestamp: 2017-07-06 12:30:00+00:00, Price: 8.22
Timestamp: 2017-07-06 12:45:00+00:00, Price: 8.22
Timestamp: 2017-07-06 13:00:00+00:00, Price: 8.22
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Lower Outliers for TUPRS in 2017/8
Timestamp: 2017-08-01 06:45:00+00:00, Price: 86.0293
Timestamp: 2017-08-01 07:00:00+00:00, Price: 86.4262
Timestamp: 2017-08-01 07:15:00+00:00, Price: 86.1881
Timestamp: 2017-08-01 07:30:00+00:00, Price: 86.0293
Timestamp: 2017-08-01 07:45:00+00:00, Price: 86.0293
Timestamp: 2017-08-01 08:00:00+00:00, Price: 86.0293
Timestamp: 2017-08-01 08:15:00+00:00, Price: 85.8706
Timestamp: 2017-08-01 08:30:00+00:00, Price: 85.7912
Timestamp: 2017-08-01 08:45:00+00:00, Price: 85.6325
Timestamp: 2017-08-01 09:00:00+00:00, Price: 84.2834
Timestamp: 2017-08-01 09:15:00+00:00, Price: 84.7596
Timestamp: 2017-08-01 09:30:00+00:00, Price: 84.7596
Timestamp: 2017-08-01 09:45:00+00:00, Price: 84.6008
Timestamp: 2017-08-01 10:45:00+00:00, Price: 84.6008
Timestamp: 2017-08-01 11:00:00+00:00, Price: 84.5215
Timestamp: 2017-08-01 11:15:00+00:00, Price: 84.9977
Timestamp: 2017-08-01 11:30:00+00:00, Price: 84.9183
Timestamp: 2017-08-01 11:45:00+00:00, Price: 84.9183
Timestamp: 2017-08-01 12:00:00+00:00, Price: 84.9977
Timestamp: 2017-08-01 12:15:00+00:00, Price: 84.6008
Timestamp: 2017-08-01 12:30:00+00:00, Price: 84.4421
Timestamp: 2017-08-01 12:45:00+00:00, Price: 84.204
Timestamp: 2017-08-01 13:00:00+00:00, Price: 84.3627
Timestamp: 2017-08-01 13:15:00+00:00, Price: 84.4421
Timestamp: 2017-08-01 13:30:00+00:00, Price: 84.2834
Timestamp: 2017-08-01 13:45:00+00:00, Price: 84.204
Timestamp: 2017-08-01 14:00:00+00:00, Price: 84.0453
Timestamp: 2017-08-01 14:15:00+00:00, Price: 84.0453
Timestamp: 2017-08-01 14:30:00+00:00, Price: 84.1246
Timestamp: 2017-08-01 14:45:00+00:00, Price: 85.1564
Timestamp: 2017-08-01 15:00:00+00:00, Price: 84.9183
Timestamp: 2017-08-02 06:45:00+00:00, Price: 84.7596
Timestamp: 2017-08-02 07:00:00+00:00, Price: 85.3945
Timestamp: 2017-08-02 07:15:00+00:00, Price: 85.95
Timestamp: 2017-08-02 07:30:00+00:00, Price: 86.0293
Timestamp: 2017-08-02 07:45:00+00:00, Price: 85.8706
Timestamp: 2017-08-02 08:00:00+00:00, Price: 85.8706
Timestamp: 2017-08-02 08:15:00+00:00, Price: 86.1087
Timestamp: 2017-08-02 08:30:00+00:00, Price: 86.1881
Timestamp: 2017-08-02 08:45:00+00:00, Price: 86.3468
Timestamp: 2017-08-02 09:00:00+00:00, Price: 86.5849
Timestamp: 2017-08-02 09:15:00+00:00, Price: 86.9024
Timestamp: 2017-08-02 09:30:00+00:00, Price: 86.9817
Timestamp: 2017-08-02 09:45:00+00:00, Price: 86.823
Timestamp: 2017-08-02 10:45:00+00:00, Price: 86.823
Timestamp: 2017-08-02 11:00:00+00:00, Price: 86.6643
Timestamp: 2017-08-02 11:15:00+00:00, Price: 86.4262
Timestamp: 2017-08-02 11:30:00+00:00, Price: 85.7119
Timestamp: 2017-08-02 11:45:00+00:00, Price: 85.3151
Timestamp: 2017-08-02 12:00:00+00:00, Price: 85.5532
Timestamp: 2017-08-02 12:15:00+00:00, Price: 85.5532
Timestamp: 2017-08-02 12:30:00+00:00, Price: 85.4738
Timestamp: 2017-08-02 12:45:00+00:00, Price: 85.7912
Timestamp: 2017-08-02 13:00:00+00:00, Price: 85.8706
Timestamp: 2017-08-02 13:15:00+00:00, Price: 85.7119
Timestamp: 2017-08-02 13:30:00+00:00, Price: 85.5532
Timestamp: 2017-08-02 13:45:00+00:00, Price: 85.6325
Timestamp: 2017-08-02 14:00:00+00:00, Price: 86.0293
Timestamp: 2017-08-02 14:15:00+00:00, Price: 85.95
Timestamp: 2017-08-02 14:30:00+00:00, Price: 85.7119
Timestamp: 2017-08-02 14:45:00+00:00, Price: 86.0293
Timestamp: 2017-08-02 15:00:00+00:00, Price: 85.7119
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Upper Outliers for VAKBN in 2017/9
Timestamp: 2017-09-05 06:45:00+00:00, Price: 7.118
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Upper Outliers for AKBNK in 2017/10
Timestamp: 2017-10-31 12:45:00+00:00, Price: 8.1925
Timestamp: 2017-10-31 13:00:00+00:00, Price: 8.1925
Timestamp: 2017-10-31 13:15:00+00:00, Price: 8.1925
Timestamp: 2017-10-31 13:30:00+00:00, Price: 8.2007
Timestamp: 2017-10-31 13:45:00+00:00, Price: 8.1925
Timestamp: 2017-10-31 14:00:00+00:00, Price: 8.1925
Timestamp: 2017-10-31 14:30:00+00:00, Price: 8.2007
Timestamp: 2017-10-31 14:45:00+00:00, Price: 8.2007
Timestamp: 2017-10-31 15:00:00+00:00, Price: 8.2336
Lower Outliers for AKBNK in 2017/10
Timestamp: 2017-10-09 06:45:00+00:00, Price: 7.518
Timestamp: 2017-10-09 07:00:00+00:00, Price: 7.3946
Timestamp: 2017-10-09 07:15:00+00:00, Price: 7.4769
Timestamp: 2017-10-09 07:30:00+00:00, Price: 7.4357
Timestamp: 2017-10-09 07:45:00+00:00, Price: 7.4439
Timestamp: 2017-10-09 08:00:00+00:00, Price: 7.4357
Timestamp: 2017-10-09 08:15:00+00:00, Price: 7.4192
Timestamp: 2017-10-09 08:30:00+00:00, Price: 7.4275
Timestamp: 2017-10-09 08:45:00+00:00, Price: 7.4192
Timestamp: 2017-10-09 09:00:00+00:00, Price: 7.4275
Timestamp: 2017-10-09 09:15:00+00:00, Price: 7.4357
Timestamp: 2017-10-09 09:30:00+00:00, Price: 7.4604
Timestamp: 2017-10-09 09:45:00+00:00, Price: 7.4769
Timestamp: 2017-10-09 10:00:00+00:00, Price: 7.4769
Timestamp: 2017-10-09 10:45:00+00:00, Price: 7.4769
Timestamp: 2017-10-09 11:00:00+00:00, Price: 7.4686
Timestamp: 2017-10-09 11:15:00+00:00, Price: 7.4933
Timestamp: 2017-10-09 11:30:00+00:00, Price: 7.485
Timestamp: 2017-10-09 11:45:00+00:00, Price: 7.5015
Timestamp: 2017-10-09 12:00:00+00:00, Price: 7.4769
Timestamp: 2017-10-09 12:15:00+00:00, Price: 7.5097
Timestamp: 2017-10-09 12:30:00+00:00, Price: 7.5097
Timestamp: 2017-10-09 12:45:00+00:00, Price: 7.518
Timestamp: 2017-10-09 13:00:00+00:00, Price: 7.518
Timestamp: 2017-10-09 13:15:00+00:00, Price: 7.518
Timestamp: 2017-10-09 13:30:00+00:00, Price: 7.518
Timestamp: 2017-10-09 13:45:00+00:00, Price: 7.518
Timestamp: 2017-10-09 14:00:00+00:00, Price: 7.5344
Timestamp: 2017-10-09 14:15:00+00:00, Price: 7.5427
Timestamp: 2017-10-09 14:30:00+00:00, Price: 7.5591
Timestamp: 2017-10-09 14:45:00+00:00, Price: 7.5508
Timestamp: 2017-10-09 15:00:00+00:00, Price: 7.5591
Upper Outliers for VAKBN in 2017/10
Timestamp: 2017-10-19 14:15:00+00:00, Price: 6.3885
Timestamp: 2017-10-19 14:30:00+00:00, Price: 6.3983
Timestamp: 2017-10-20 06:45:00+00:00, Price: 6.3786
Timestamp: 2017-10-20 07:00:00+00:00, Price: 6.3786
Timestamp: 2017-10-20 07:15:00+00:00, Price: 6.3983
Timestamp: 2017-10-20 07:30:00+00:00, Price: 6.4377
Timestamp: 2017-10-20 07:45:00+00:00, Price: 6.5068
Timestamp: 2017-10-20 08:00:00+00:00, Price: 6.5265
Timestamp: 2017-10-20 08:15:00+00:00, Price: 6.5068
Timestamp: 2017-10-20 08:30:00+00:00, Price: 6.487
Timestamp: 2017-10-20 08:45:00+00:00, Price: 6.4969
Timestamp: 2017-10-20 09:00:00+00:00, Price: 6.487
Timestamp: 2017-10-20 09:15:00+00:00, Price: 6.4969
Timestamp: 2017-10-20 09:30:00+00:00, Price: 6.487
Timestamp: 2017-10-20 09:45:00+00:00, Price: 6.4772
Timestamp: 2017-10-20 10:45:00+00:00, Price: 6.4772
Timestamp: 2017-10-20 11:00:00+00:00, Price: 6.4772
Timestamp: 2017-10-20 11:15:00+00:00, Price: 6.4772
Timestamp: 2017-10-20 11:30:00+00:00, Price: 6.4673
Timestamp: 2017-10-20 11:45:00+00:00, Price: 6.4575
Timestamp: 2017-10-20 12:00:00+00:00, Price: 6.4575
Timestamp: 2017-10-20 12:15:00+00:00, Price: 6.4476
Timestamp: 2017-10-20 12:30:00+00:00, Price: 6.418
Timestamp: 2017-10-20 13:00:00+00:00, Price: 6.3786
Timestamp: 2017-10-20 13:15:00+00:00, Price: 6.3589
Timestamp: 2017-10-20 14:45:00+00:00, Price: 6.3687
Timestamp: 2017-10-20 15:00:00+00:00, Price: 6.3589
Timestamp: 2017-10-26 13:45:00+00:00, Price: 6.3687
Lower Outliers for VAKBN in 2017/10
Timestamp: 2017-10-09 06:45:00+00:00, Price: 5.8955
Timestamp: 2017-10-09 07:00:00+00:00, Price: 5.8561
Timestamp: 2017-10-09 07:15:00+00:00, Price: 5.8758
Timestamp: 2017-10-09 07:30:00+00:00, Price: 5.8462
Timestamp: 2017-10-09 07:45:00+00:00, Price: 5.8462
Timestamp: 2017-10-09 08:00:00+00:00, Price: 5.8561
Timestamp: 2017-10-09 08:15:00+00:00, Price: 5.8364
Timestamp: 2017-10-09 08:30:00+00:00, Price: 5.8364
Timestamp: 2017-10-09 08:45:00+00:00, Price: 5.8364
Timestamp: 2017-10-09 09:00:00+00:00, Price: 5.8364
Timestamp: 2017-10-09 09:15:00+00:00, Price: 5.8364
Timestamp: 2017-10-09 09:30:00+00:00, Price: 5.8462
Timestamp: 2017-10-09 09:45:00+00:00, Price: 5.8561
Timestamp: 2017-10-09 10:00:00+00:00, Price: 5.85115
Timestamp: 2017-10-09 10:45:00+00:00, Price: 5.8462
Timestamp: 2017-10-09 11:00:00+00:00, Price: 5.8364
Timestamp: 2017-10-09 11:15:00+00:00, Price: 5.8659
Timestamp: 2017-10-09 11:30:00+00:00, Price: 5.8561
Timestamp: 2017-10-09 11:45:00+00:00, Price: 5.8857
Timestamp: 2017-10-09 12:00:00+00:00, Price: 5.8561
Timestamp: 2017-10-09 12:15:00+00:00, Price: 5.8758
Timestamp: 2017-10-09 12:30:00+00:00, Price: 5.8659
Timestamp: 2017-10-09 12:45:00+00:00, Price: 5.8758
Timestamp: 2017-10-09 13:00:00+00:00, Price: 5.8857
Timestamp: 2017-10-09 13:15:00+00:00, Price: 5.8857
Timestamp: 2017-10-09 13:30:00+00:00, Price: 5.8857
Timestamp: 2017-10-09 13:45:00+00:00, Price: 5.8659
Timestamp: 2017-10-09 14:00:00+00:00, Price: 5.8758
Timestamp: 2017-10-09 14:15:00+00:00, Price: 5.8955
Timestamp: 2017-10-09 14:30:00+00:00, Price: 5.8955
Timestamp: 2017-10-09 14:45:00+00:00, Price: 5.8857
Timestamp: 2017-10-09 15:00:00+00:00, Price: 5.8955
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Upper Outliers for VAKBN in 2017/11
Timestamp: 2017-11-01 11:00:00+00:00, Price: 6.5363
Timestamp: 2017-11-01 11:15:00+00:00, Price: 6.5462
Timestamp: 2017-11-01 11:30:00+00:00, Price: 6.5363
Timestamp: 2017-11-02 06:45:00+00:00, Price: 6.5363
Timestamp: 2017-11-02 07:00:00+00:00, Price: 6.5856
Timestamp: 2017-11-02 07:15:00+00:00, Price: 6.5265
Timestamp: 2017-11-02 07:30:00+00:00, Price: 6.5659
Timestamp: 2017-11-02 07:45:00+00:00, Price: 6.6251
Timestamp: 2017-11-02 08:00:00+00:00, Price: 6.5856
Timestamp: 2017-11-02 08:15:00+00:00, Price: 6.5659
Timestamp: 2017-11-02 08:30:00+00:00, Price: 6.5856
Timestamp: 2017-11-02 08:45:00+00:00, Price: 6.5856
Timestamp: 2017-11-02 09:00:00+00:00, Price: 6.5758
Timestamp: 2017-11-02 09:15:00+00:00, Price: 6.5856
Timestamp: 2017-11-02 09:30:00+00:00, Price: 6.5856
Timestamp: 2017-11-02 09:45:00+00:00, Price: 6.5758
Timestamp: 2017-11-02 10:45:00+00:00, Price: 6.5856
Timestamp: 2017-11-02 11:00:00+00:00, Price: 6.5856
Timestamp: 2017-11-02 11:15:00+00:00, Price: 6.5856
Timestamp: 2017-11-02 11:30:00+00:00, Price: 6.5659
Timestamp: 2017-11-02 11:45:00+00:00, Price: 6.5265
Timestamp: 2017-11-02 12:30:00+00:00, Price: 6.5363
Timestamp: 2017-11-02 12:45:00+00:00, Price: 6.5462
NO OUTLIERS
NO OUTLIERS
Upper Outliers for TCELL in 2017/11
Timestamp: 2017-11-01 14:00:00+00:00, Price: 13.2952
Timestamp: 2017-11-01 14:30:00+00:00, Price: 13.3043
Timestamp: 2017-11-01 14:45:00+00:00, Price: 13.4123
Timestamp: 2017-11-01 15:00:00+00:00, Price: 13.4303
Timestamp: 2017-11-02 06:45:00+00:00, Price: 13.6104
Timestamp: 2017-11-02 07:00:00+00:00, Price: 13.7186
Timestamp: 2017-11-02 07:15:00+00:00, Price: 13.6014
Timestamp: 2017-11-02 07:30:00+00:00, Price: 13.6014
Timestamp: 2017-11-02 07:45:00+00:00, Price: 13.6374
Timestamp: 2017-11-02 08:00:00+00:00, Price: 13.5563
Timestamp: 2017-11-02 08:15:00+00:00, Price: 13.4753
Timestamp: 2017-11-02 08:30:00+00:00, Price: 13.4663
Timestamp: 2017-11-02 08:45:00+00:00, Price: 13.3311
Timestamp: 2017-11-02 09:00:00+00:00, Price: 13.3492
Timestamp: 2017-11-02 09:30:00+00:00, Price: 13.2861
Timestamp: 2017-11-02 09:45:00+00:00, Price: 13.3582
Timestamp: 2017-11-02 10:45:00+00:00, Price: 13.5384
Timestamp: 2017-11-02 11:00:00+00:00, Price: 13.3311
Timestamp: 2017-11-02 11:15:00+00:00, Price: 13.3131
Timestamp: 2017-11-30 11:45:00+00:00, Price: 13.3402
Timestamp: 2017-11-30 12:00:00+00:00, Price: 13.4573
Timestamp: 2017-11-30 12:15:00+00:00, Price: 13.4663
Timestamp: 2017-11-30 12:30:00+00:00, Price: 13.5294
Timestamp: 2017-11-30 12:45:00+00:00, Price: 13.5203
Timestamp: 2017-11-30 13:00:00+00:00, Price: 13.5113
Timestamp: 2017-11-30 13:15:00+00:00, Price: 13.5834
Timestamp: 2017-11-30 13:30:00+00:00, Price: 13.5384
Timestamp: 2017-11-30 13:45:00+00:00, Price: 13.5834
Timestamp: 2017-11-30 14:00:00+00:00, Price: 13.6824
Timestamp: 2017-11-30 14:15:00+00:00, Price: 13.7095
Timestamp: 2017-11-30 14:30:00+00:00, Price: 13.6284
Timestamp: 2017-11-30 14:45:00+00:00, Price: 13.6555
Timestamp: 2017-11-30 15:00:00+00:00, Price: 13.7005
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Upper Outliers for ARCLK in 2017/12
Timestamp: 2017-12-28 14:30:00+00:00, Price: 20.289
Timestamp: 2017-12-28 14:45:00+00:00, Price: 20.289
Timestamp: 2017-12-28 15:00:00+00:00, Price: 20.3276
Timestamp: 2017-12-29 06:45:00+00:00, Price: 20.4048
Timestamp: 2017-12-29 07:00:00+00:00, Price: 20.4048
Timestamp: 2017-12-29 07:15:00+00:00, Price: 20.4627
Timestamp: 2017-12-29 07:30:00+00:00, Price: 20.4241
Timestamp: 2017-12-29 07:45:00+00:00, Price: 20.4048
Timestamp: 2017-12-29 08:00:00+00:00, Price: 20.3855
Timestamp: 2017-12-29 08:15:00+00:00, Price: 20.4241
Timestamp: 2017-12-29 08:30:00+00:00, Price: 20.4048
Timestamp: 2017-12-29 08:45:00+00:00, Price: 20.3855
Timestamp: 2017-12-29 09:00:00+00:00, Price: 20.4241
Timestamp: 2017-12-29 09:15:00+00:00, Price: 20.4048
Timestamp: 2017-12-29 09:30:00+00:00, Price: 20.4241
Timestamp: 2017-12-29 09:45:00+00:00, Price: 20.3855
Timestamp: 2017-12-29 10:00:00+00:00, Price: 20.39515
Timestamp: 2017-12-29 10:45:00+00:00, Price: 20.4048
Timestamp: 2017-12-29 11:00:00+00:00, Price: 20.4048
Timestamp: 2017-12-29 11:15:00+00:00, Price: 20.4241
Timestamp: 2017-12-29 11:30:00+00:00, Price: 20.4241
Timestamp: 2017-12-29 11:45:00+00:00, Price: 20.4241
Timestamp: 2017-12-29 12:00:00+00:00, Price: 20.4241
Timestamp: 2017-12-29 12:15:00+00:00, Price: 20.4048
Timestamp: 2017-12-29 12:30:00+00:00, Price: 20.4241
Timestamp: 2017-12-29 12:45:00+00:00, Price: 20.4241
Timestamp: 2017-12-29 13:00:00+00:00, Price: 20.4241
Timestamp: 2017-12-29 13:15:00+00:00, Price: 20.4241
Timestamp: 2017-12-29 13:30:00+00:00, Price: 20.4434
Timestamp: 2017-12-29 13:45:00+00:00, Price: 20.5013
Timestamp: 2017-12-29 14:00:00+00:00, Price: 20.4627
Timestamp: 2017-12-29 14:15:00+00:00, Price: 20.5013
Timestamp: 2017-12-29 14:30:00+00:00, Price: 20.6751
Timestamp: 2017-12-29 14:45:00+00:00, Price: 20.7137
Timestamp: 2017-12-29 15:00:00+00:00, Price: 20.7716
Lower Outliers for ARCLK in 2017/12
Timestamp: 2017-12-01 06:45:00+00:00, Price: 19.1114
Timestamp: 2017-12-01 07:00:00+00:00, Price: 19.3045
Timestamp: 2017-12-01 07:15:00+00:00, Price: 19.3817
Timestamp: 2017-12-01 07:30:00+00:00, Price: 19.401
Timestamp: 2017-12-01 07:45:00+00:00, Price: 19.401
Upper Outliers for TUPRS in 2017/12
Timestamp: 2017-12-21 07:15:00+00:00, Price: 98.5687
Timestamp: 2017-12-21 07:30:00+00:00, Price: 99.1243
Timestamp: 2017-12-21 07:45:00+00:00, Price: 99.3624
Lower Outliers for TUPRS in 2017/12
Timestamp: 2017-12-15 07:00:00+00:00, Price: 90.0769
Timestamp: 2017-12-18 08:30:00+00:00, Price: 90.4737
Timestamp: 2017-12-18 08:45:00+00:00, Price: 90.4737
Timestamp: 2017-12-18 09:00:00+00:00, Price: 90.3943
Timestamp: 2017-12-18 09:15:00+00:00, Price: 90.2356
Timestamp: 2017-12-18 09:30:00+00:00, Price: 90.1562
Timestamp: 2017-12-18 09:45:00+00:00, Price: 90.0769
Timestamp: 2017-12-18 10:00:00+00:00, Price: 90.0769
Timestamp: 2017-12-18 10:45:00+00:00, Price: 90.0769
Timestamp: 2017-12-18 11:00:00+00:00, Price: 89.9975
Timestamp: 2017-12-18 11:15:00+00:00, Price: 89.1245
Timestamp: 2017-12-18 11:30:00+00:00, Price: 89.6007
Timestamp: 2017-12-18 11:45:00+00:00, Price: 89.7594
Timestamp: 2017-12-18 12:00:00+00:00, Price: 90.315
Timestamp: 2017-12-18 12:15:00+00:00, Price: 90.1562
Timestamp: 2017-12-18 12:30:00+00:00, Price: 90.3943
Timestamp: 2017-12-18 12:45:00+00:00, Price: 90.3943
Timestamp: 2017-12-18 13:00:00+00:00, Price: 90.3943
Timestamp: 2017-12-18 13:15:00+00:00, Price: 90.4737
Timestamp: 2017-12-18 13:30:00+00:00, Price: 90.3943
Timestamp: 2017-12-18 13:45:00+00:00, Price: 89.9975
Timestamp: 2017-12-18 14:00:00+00:00, Price: 89.9181
Timestamp: 2017-12-18 14:15:00+00:00, Price: 89.2038
Timestamp: 2017-12-18 14:30:00+00:00, Price: 89.4419
Timestamp: 2017-12-18 14:45:00+00:00, Price: 89.3626
Timestamp: 2017-12-18 15:00:00+00:00, Price: 89.2038
Timestamp: 2017-12-19 06:45:00+00:00, Price: 89.3626
Timestamp: 2017-12-19 07:00:00+00:00, Price: 89.3626
Timestamp: 2017-12-19 07:15:00+00:00, Price: 88.9658
Timestamp: 2017-12-19 07:30:00+00:00, Price: 89.0451
Timestamp: 2017-12-19 07:45:00+00:00, Price: 88.9658
Timestamp: 2017-12-19 08:00:00+00:00, Price: 90.0769
Timestamp: 2017-12-19 08:30:00+00:00, Price: 90.4737
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Upper Outliers for AKBNK in 2018/2
Timestamp: 2018-02-01 09:45:00+00:00, Price: 9.0397
Timestamp: 2018-02-01 10:45:00+00:00, Price: 9.0397
Lower Outliers for AKBNK in 2018/2
Timestamp: 2018-02-09 07:15:00+00:00, Price: 8.2665
Timestamp: 2018-02-09 07:45:00+00:00, Price: 8.2583
Timestamp: 2018-02-09 12:30:00+00:00, Price: 8.2665
Timestamp: 2018-02-09 12:45:00+00:00, Price: 8.2336
Timestamp: 2018-02-09 13:00:00+00:00, Price: 8.2665
Upper Outliers for VAKBN in 2018/2
Timestamp: 2018-02-01 07:30:00+00:00, Price: 7.5222
Timestamp: 2018-02-01 07:45:00+00:00, Price: 7.5321
Timestamp: 2018-02-01 08:00:00+00:00, Price: 7.5025
Timestamp: 2018-02-01 08:15:00+00:00, Price: 7.4828
Timestamp: 2018-02-01 08:30:00+00:00, Price: 7.5123
Timestamp: 2018-02-01 08:45:00+00:00, Price: 7.4828
Timestamp: 2018-02-01 09:00:00+00:00, Price: 7.4828
Timestamp: 2018-02-01 09:15:00+00:00, Price: 7.4828
Upper Outliers for ARCLK in 2018/2
Timestamp: 2018-02-01 12:45:00+00:00, Price: 18.8508
Timestamp: 2018-02-01 13:00:00+00:00, Price: 18.8412
Timestamp: 2018-02-01 13:15:00+00:00, Price: 18.8412
Timestamp: 2018-02-05 08:30:00+00:00, Price: 18.9377
Timestamp: 2018-02-05 08:45:00+00:00, Price: 18.957
Timestamp: 2018-02-05 09:00:00+00:00, Price: 18.9859
Timestamp: 2018-02-05 09:15:00+00:00, Price: 18.9859
Timestamp: 2018-02-05 09:30:00+00:00, Price: 18.9377
Timestamp: 2018-02-05 09:45:00+00:00, Price: 18.8508
Timestamp: 2018-02-05 10:45:00+00:00, Price: 18.8508
Timestamp: 2018-02-05 11:00:00+00:00, Price: 18.8218
Timestamp: 2018-02-05 11:15:00+00:00, Price: 18.8508
Timestamp: 2018-02-05 11:30:00+00:00, Price: 18.8991
Timestamp: 2018-02-05 11:45:00+00:00, Price: 18.9184
Timestamp: 2018-02-05 12:00:00+00:00, Price: 18.8508
Timestamp: 2018-02-05 12:15:00+00:00, Price: 18.8412
Lower Outliers for TUPRS in 2018/2
Timestamp: 2018-02-06 06:45:00+00:00, Price: 84.9183
Upper Outliers for TCELL in 2018/2
Timestamp: 2018-02-01 07:00:00+00:00, Price: 14.6434
Timestamp: 2018-02-01 07:15:00+00:00, Price: 14.6434
Timestamp: 2018-02-01 08:30:00+00:00, Price: 14.6248
Timestamp: 2018-02-01 09:45:00+00:00, Price: 14.6248
Timestamp: 2018-02-01 10:45:00+00:00, Price: 14.634
Lower Outliers for TCELL in 2018/2
Timestamp: 2018-02-20 14:45:00+00:00, Price: 13.5483
Timestamp: 2018-02-20 15:00:00+00:00, Price: 13.5576
Timestamp: 2018-02-21 13:45:00+00:00, Price: 13.7153
Upper Outliers for THYAO in 2018/2
Timestamp: 2018-02-26 14:45:00+00:00, Price: 18.89
Timestamp: 2018-02-26 15:00:00+00:00, Price: 18.92
Timestamp: 2018-02-27 06:45:00+00:00, Price: 18.92
Timestamp: 2018-02-27 09:45:00+00:00, Price: 18.85
Timestamp: 2018-02-27 10:45:00+00:00, Price: 18.84
Timestamp: 2018-02-27 11:00:00+00:00, Price: 18.83
Timestamp: 2018-02-28 09:00:00+00:00, Price: 18.85
Timestamp: 2018-02-28 09:15:00+00:00, Price: 18.84
Timestamp: 2018-02-28 09:30:00+00:00, Price: 18.84
Timestamp: 2018-02-28 09:45:00+00:00, Price: 18.86
Timestamp: 2018-02-28 10:00:00+00:00, Price: 18.855
Timestamp: 2018-02-28 10:45:00+00:00, Price: 18.85
Timestamp: 2018-02-28 11:00:00+00:00, Price: 18.85
Timestamp: 2018-02-28 11:15:00+00:00, Price: 18.83
Timestamp: 2018-02-28 12:15:00+00:00, Price: 18.91
Timestamp: 2018-02-28 12:30:00+00:00, Price: 18.9
Timestamp: 2018-02-28 12:45:00+00:00, Price: 18.91
Timestamp: 2018-02-28 13:00:00+00:00, Price: 18.92
Timestamp: 2018-02-28 13:15:00+00:00, Price: 18.94
Timestamp: 2018-02-28 13:30:00+00:00, Price: 18.95
Timestamp: 2018-02-28 13:45:00+00:00, Price: 18.96
Timestamp: 2018-02-28 14:00:00+00:00, Price: 18.97
Timestamp: 2018-02-28 14:15:00+00:00, Price: 19.02
Timestamp: 2018-02-28 14:30:00+00:00, Price: 19.0
Timestamp: 2018-02-28 14:45:00+00:00, Price: 19.01
Timestamp: 2018-02-28 15:00:00+00:00, Price: 19.07
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Upper Outliers for ARCLK in 2018/5
Timestamp: 2018-05-02 06:45:00+00:00, Price: 18.08
Timestamp: 2018-05-02 07:00:00+00:00, Price: 18.35
Timestamp: 2018-05-02 07:15:00+00:00, Price: 18.67
Timestamp: 2018-05-02 07:30:00+00:00, Price: 18.7
Timestamp: 2018-05-02 07:45:00+00:00, Price: 18.57
Timestamp: 2018-05-02 08:00:00+00:00, Price: 18.65
Timestamp: 2018-05-02 08:15:00+00:00, Price: 18.57
Timestamp: 2018-05-02 08:30:00+00:00, Price: 18.38
Timestamp: 2018-05-02 08:45:00+00:00, Price: 18.49
Timestamp: 2018-05-02 09:00:00+00:00, Price: 18.27
Timestamp: 2018-05-02 09:15:00+00:00, Price: 18.35
Timestamp: 2018-05-02 09:30:00+00:00, Price: 18.33
Timestamp: 2018-05-02 09:45:00+00:00, Price: 18.33
Timestamp: 2018-05-02 10:00:00+00:00, Price: 18.29
Timestamp: 2018-05-02 10:45:00+00:00, Price: 18.25
Timestamp: 2018-05-02 11:00:00+00:00, Price: 18.12
Timestamp: 2018-05-02 11:15:00+00:00, Price: 18.22
Timestamp: 2018-05-02 11:30:00+00:00, Price: 18.45
Timestamp: 2018-05-02 11:45:00+00:00, Price: 18.42
Timestamp: 2018-05-02 12:00:00+00:00, Price: 18.46
Timestamp: 2018-05-02 12:15:00+00:00, Price: 18.41
Timestamp: 2018-05-02 12:30:00+00:00, Price: 18.41
Timestamp: 2018-05-02 12:45:00+00:00, Price: 18.41
Timestamp: 2018-05-02 13:00:00+00:00, Price: 18.43
Timestamp: 2018-05-02 13:15:00+00:00, Price: 18.35
Timestamp: 2018-05-02 13:30:00+00:00, Price: 18.22
Timestamp: 2018-05-02 13:45:00+00:00, Price: 18.17
Timestamp: 2018-05-02 14:00:00+00:00, Price: 18.18
Timestamp: 2018-05-02 14:15:00+00:00, Price: 18.17
Timestamp: 2018-05-02 14:30:00+00:00, Price: 18.16
Timestamp: 2018-05-02 14:45:00+00:00, Price: 18.29
Timestamp: 2018-05-02 15:00:00+00:00, Price: 18.32
Timestamp: 2018-05-03 06:45:00+00:00, Price: 18.32
Timestamp: 2018-05-03 07:00:00+00:00, Price: 18.17
Timestamp: 2018-05-03 07:15:00+00:00, Price: 18.05
Timestamp: 2018-05-03 07:30:00+00:00, Price: 18.01
Timestamp: 2018-05-03 07:45:00+00:00, Price: 18.04
Timestamp: 2018-05-03 08:00:00+00:00, Price: 18.1
Timestamp: 2018-05-03 08:15:00+00:00, Price: 18.06
Timestamp: 2018-05-03 08:30:00+00:00, Price: 18.05
Timestamp: 2018-05-03 08:45:00+00:00, Price: 17.97
Timestamp: 2018-05-03 09:00:00+00:00, Price: 18.01
Timestamp: 2018-05-03 09:15:00+00:00, Price: 18.01
Timestamp: 2018-05-03 09:30:00+00:00, Price: 18.03
Timestamp: 2018-05-03 09:45:00+00:00, Price: 18.02
Timestamp: 2018-05-03 10:00:00+00:00, Price: 18.02
Timestamp: 2018-05-03 10:45:00+00:00, Price: 18.02
Timestamp: 2018-05-03 11:00:00+00:00, Price: 18.03
Timestamp: 2018-05-03 11:15:00+00:00, Price: 17.99
Timestamp: 2018-05-03 11:30:00+00:00, Price: 18.03
Timestamp: 2018-05-03 11:45:00+00:00, Price: 18.03
Timestamp: 2018-05-03 12:00:00+00:00, Price: 18.02
Timestamp: 2018-05-03 12:15:00+00:00, Price: 17.99
Timestamp: 2018-05-03 12:30:00+00:00, Price: 18.03
Timestamp: 2018-05-03 12:45:00+00:00, Price: 18.01
Timestamp: 2018-05-03 13:00:00+00:00, Price: 18.1
Timestamp: 2018-05-03 13:15:00+00:00, Price: 18.2
Timestamp: 2018-05-03 13:30:00+00:00, Price: 18.2
Timestamp: 2018-05-03 13:45:00+00:00, Price: 18.19
Timestamp: 2018-05-03 14:00:00+00:00, Price: 17.97
Timestamp: 2018-05-04 07:00:00+00:00, Price: 17.97
Upper Outliers for TUPRS in 2018/5
Timestamp: 2018-05-29 15:00:00+00:00, Price: 96.5579
Lower Outliers for TUPRS in 2018/5
Timestamp: 2018-05-10 06:45:00+00:00, Price: 84.0411
Timestamp: 2018-05-10 07:00:00+00:00, Price: 84.354
Timestamp: 2018-05-10 07:15:00+00:00, Price: 85.0246
Timestamp: 2018-05-10 07:30:00+00:00, Price: 84.4435
Timestamp: 2018-05-10 07:45:00+00:00, Price: 83.5047
Timestamp: 2018-05-10 08:00:00+00:00, Price: 83.5047
Timestamp: 2018-05-10 08:15:00+00:00, Price: 83.9964
Timestamp: 2018-05-10 08:30:00+00:00, Price: 84.0858
Timestamp: 2018-05-10 08:45:00+00:00, Price: 83.8623
Timestamp: 2018-05-10 09:00:00+00:00, Price: 83.9517
Timestamp: 2018-05-10 09:15:00+00:00, Price: 84.0411
Timestamp: 2018-05-10 09:30:00+00:00, Price: 84.354
Timestamp: 2018-05-10 09:45:00+00:00, Price: 84.354
Timestamp: 2018-05-10 10:45:00+00:00, Price: 84.5776
Upper Outliers for TCELL in 2018/5
Timestamp: 2018-05-02 06:45:00+00:00, Price: 12.9823
Timestamp: 2018-05-02 07:00:00+00:00, Price: 12.8987
Timestamp: 2018-05-02 07:15:00+00:00, Price: 12.9173
Timestamp: 2018-05-02 07:30:00+00:00, Price: 12.8338
Timestamp: 2018-05-02 07:45:00+00:00, Price: 12.8152
Timestamp: 2018-05-02 08:00:00+00:00, Price: 12.8616
Timestamp: 2018-05-02 08:15:00+00:00, Price: 12.908
Timestamp: 2018-05-02 08:30:00+00:00, Price: 12.8431
Timestamp: 2018-05-02 08:45:00+00:00, Price: 12.8245
Timestamp: 2018-05-02 09:00:00+00:00, Price: 12.8524
Timestamp: 2018-05-02 09:15:00+00:00, Price: 12.8524
Timestamp: 2018-05-02 09:30:00+00:00, Price: 12.8802
Timestamp: 2018-05-02 09:45:00+00:00, Price: 12.8709
Timestamp: 2018-05-02 10:00:00+00:00, Price: 12.843050000000002
Timestamp: 2018-05-02 10:45:00+00:00, Price: 12.8152
Timestamp: 2018-05-02 11:00:00+00:00, Price: 12.7874
Timestamp: 2018-05-02 11:15:00+00:00, Price: 12.806
Timestamp: 2018-05-02 11:30:00+00:00, Price: 12.6853
Timestamp: 2018-05-02 11:45:00+00:00, Price: 12.6017
Timestamp: 2018-05-02 12:00:00+00:00, Price: 12.6203
Timestamp: 2018-05-02 12:15:00+00:00, Price: 12.6203
Timestamp: 2018-05-02 12:30:00+00:00, Price: 12.5646
Timestamp: 2018-05-02 12:45:00+00:00, Price: 12.6203
Timestamp: 2018-05-02 13:00:00+00:00, Price: 12.6389
Timestamp: 2018-05-02 13:15:00+00:00, Price: 12.6111
Timestamp: 2018-05-02 13:30:00+00:00, Price: 12.6111
Timestamp: 2018-05-02 13:45:00+00:00, Price: 12.5555
Timestamp: 2018-05-02 14:00:00+00:00, Price: 12.6111
Timestamp: 2018-05-02 14:15:00+00:00, Price: 12.6389
Timestamp: 2018-05-02 14:30:00+00:00, Price: 12.6389
Timestamp: 2018-05-02 14:45:00+00:00, Price: 12.6111
Timestamp: 2018-05-02 15:00:00+00:00, Price: 12.6017
Timestamp: 2018-05-03 06:45:00+00:00, Price: 12.6297
Timestamp: 2018-05-03 07:00:00+00:00, Price: 12.6111
Timestamp: 2018-05-03 07:15:00+00:00, Price: 12.6111
Timestamp: 2018-05-03 07:30:00+00:00, Price: 12.6482
Timestamp: 2018-05-03 07:45:00+00:00, Price: 12.6389
Timestamp: 2018-05-03 08:00:00+00:00, Price: 12.6668
Timestamp: 2018-05-03 08:15:00+00:00, Price: 12.6668
Timestamp: 2018-05-03 08:30:00+00:00, Price: 12.6668
Timestamp: 2018-05-03 08:45:00+00:00, Price: 12.6017
Timestamp: 2018-05-03 09:00:00+00:00, Price: 12.5832
Timestamp: 2018-05-03 09:15:00+00:00, Price: 12.6203
Timestamp: 2018-05-03 09:30:00+00:00, Price: 12.6017
Timestamp: 2018-05-03 09:45:00+00:00, Price: 12.6017
Timestamp: 2018-05-03 10:00:00+00:00, Price: 12.6017
Timestamp: 2018-05-03 10:45:00+00:00, Price: 12.6017
Timestamp: 2018-05-03 11:15:00+00:00, Price: 12.5555
Timestamp: 2018-05-03 11:30:00+00:00, Price: 12.574
Timestamp: 2018-05-11 06:45:00+00:00, Price: 12.6017
Lower Outliers for TCELL in 2018/5
Timestamp: 2018-05-31 08:15:00+00:00, Price: 11.5068
Timestamp: 2018-05-31 08:30:00+00:00, Price: 11.414
Timestamp: 2018-05-31 08:45:00+00:00, Price: 11.4975
Timestamp: 2018-05-31 09:00:00+00:00, Price: 11.4604
Timestamp: 2018-05-31 09:15:00+00:00, Price: 11.4326
Timestamp: 2018-05-31 09:30:00+00:00, Price: 11.414
Timestamp: 2018-05-31 09:45:00+00:00, Price: 11.414
Timestamp: 2018-05-31 10:00:00+00:00, Price: 11.41865
Timestamp: 2018-05-31 10:45:00+00:00, Price: 11.4233
Timestamp: 2018-05-31 11:00:00+00:00, Price: 11.414
Timestamp: 2018-05-31 11:15:00+00:00, Price: 11.3398
Timestamp: 2018-05-31 11:30:00+00:00, Price: 11.3583
Timestamp: 2018-05-31 11:45:00+00:00, Price: 11.3305
Timestamp: 2018-05-31 12:00:00+00:00, Price: 11.3491
Timestamp: 2018-05-31 12:15:00+00:00, Price: 11.3211
Timestamp: 2018-05-31 12:30:00+00:00, Price: 11.3491
Timestamp: 2018-05-31 12:45:00+00:00, Price: 11.3398
Timestamp: 2018-05-31 13:00:00+00:00, Price: 11.3769
Timestamp: 2018-05-31 13:15:00+00:00, Price: 11.3769
Timestamp: 2018-05-31 13:30:00+00:00, Price: 11.3398
Timestamp: 2018-05-31 13:45:00+00:00, Price: 11.3026
Timestamp: 2018-05-31 14:00:00+00:00, Price: 11.2655
Timestamp: 2018-05-31 14:15:00+00:00, Price: 11.1821
Timestamp: 2018-05-31 14:30:00+00:00, Price: 11.2006
Timestamp: 2018-05-31 14:45:00+00:00, Price: 11.2563
Timestamp: 2018-05-31 15:00:00+00:00, Price: 11.1356
Upper Outliers for THYAO in 2018/5
Timestamp: 2018-05-28 11:00:00+00:00, Price: 17.41
Timestamp: 2018-05-28 11:15:00+00:00, Price: 17.43
Timestamp: 2018-05-28 12:30:00+00:00, Price: 17.41
Timestamp: 2018-05-28 13:15:00+00:00, Price: 17.42
Timestamp: 2018-05-28 14:30:00+00:00, Price: 17.41
Timestamp: 2018-05-28 14:45:00+00:00, Price: 17.53
Timestamp: 2018-05-28 15:00:00+00:00, Price: 17.55
Timestamp: 2018-05-29 06:45:00+00:00, Price: 17.53
Timestamp: 2018-05-29 14:00:00+00:00, Price: 17.55
Timestamp: 2018-05-29 14:15:00+00:00, Price: 17.5
Timestamp: 2018-05-29 14:30:00+00:00, Price: 17.47
Timestamp: 2018-05-29 14:45:00+00:00, Price: 17.49
Timestamp: 2018-05-29 15:00:00+00:00, Price: 17.46
Timestamp: 2018-05-30 06:45:00+00:00, Price: 17.48
Timestamp: 2018-05-30 07:00:00+00:00, Price: 17.51
Timestamp: 2018-05-30 07:15:00+00:00, Price: 17.49
Timestamp: 2018-05-30 07:30:00+00:00, Price: 17.56
Timestamp: 2018-05-30 07:45:00+00:00, Price: 17.58
Timestamp: 2018-05-30 08:00:00+00:00, Price: 17.56
Timestamp: 2018-05-30 08:15:00+00:00, Price: 17.54
Timestamp: 2018-05-30 08:30:00+00:00, Price: 17.5
Timestamp: 2018-05-30 08:45:00+00:00, Price: 17.51
Timestamp: 2018-05-30 09:00:00+00:00, Price: 17.51
Timestamp: 2018-05-30 09:15:00+00:00, Price: 17.51
Timestamp: 2018-05-30 09:30:00+00:00, Price: 17.49
Timestamp: 2018-05-30 09:45:00+00:00, Price: 17.48
Timestamp: 2018-05-30 10:45:00+00:00, Price: 17.49
Timestamp: 2018-05-30 11:00:00+00:00, Price: 17.48
Timestamp: 2018-05-30 11:30:00+00:00, Price: 17.42
Timestamp: 2018-05-30 11:45:00+00:00, Price: 17.42
Timestamp: 2018-05-30 12:00:00+00:00, Price: 17.45
Timestamp: 2018-05-30 12:15:00+00:00, Price: 17.45
Timestamp: 2018-05-30 12:30:00+00:00, Price: 17.46
Timestamp: 2018-05-30 12:45:00+00:00, Price: 17.51
Timestamp: 2018-05-30 13:00:00+00:00, Price: 17.42
Timestamp: 2018-05-30 13:15:00+00:00, Price: 17.42
Timestamp: 2018-05-31 06:45:00+00:00, Price: 17.41
Timestamp: 2018-05-31 07:15:00+00:00, Price: 17.42
Timestamp: 2018-05-31 07:45:00+00:00, Price: 17.42
Timestamp: 2018-05-31 08:00:00+00:00, Price: 17.46
Timestamp: 2018-05-31 08:15:00+00:00, Price: 17.47
Lower Outliers for THYAO in 2018/5
Timestamp: 2018-05-09 07:30:00+00:00, Price: 15.6
Timestamp: 2018-05-09 08:00:00+00:00, Price: 15.93
Timestamp: 2018-05-24 13:45:00+00:00, Price: 15.79
Timestamp: 2018-05-24 14:15:00+00:00, Price: 15.89
Timestamp: 2018-05-24 14:30:00+00:00, Price: 15.76
Timestamp: 2018-05-24 14:45:00+00:00, Price: 15.66
Timestamp: 2018-05-24 15:00:00+00:00, Price: 15.66
Timestamp: 2018-05-25 06:45:00+00:00, Price: 15.95
Timestamp: 2018-05-25 07:00:00+00:00, Price: 15.84
Timestamp: 2018-05-25 07:45:00+00:00, Price: 15.91
Timestamp: 2018-05-25 08:00:00+00:00, Price: 15.87
Timestamp: 2018-05-25 08:30:00+00:00, Price: 15.94
Timestamp: 2018-05-25 09:00:00+00:00, Price: 15.9
Timestamp: 2018-05-25 09:15:00+00:00, Price: 15.85
Timestamp: 2018-05-25 09:30:00+00:00, Price: 15.86
Timestamp: 2018-05-25 09:45:00+00:00, Price: 15.85
Timestamp: 2018-05-25 10:00:00+00:00, Price: 15.86
Timestamp: 2018-05-25 10:45:00+00:00, Price: 15.87
Upper Outliers for AKBNK in 2018/6
Timestamp: 2018-06-01 06:45:00+00:00, Price: 6.8976
Timestamp: 2018-06-01 07:00:00+00:00, Price: 6.8633
Timestamp: 2018-06-04 07:45:00+00:00, Price: 6.7947
Timestamp: 2018-06-04 08:45:00+00:00, Price: 6.7861
Timestamp: 2018-06-04 09:15:00+00:00, Price: 6.8461
Timestamp: 2018-06-04 09:30:00+00:00, Price: 6.8204
Timestamp: 2018-06-04 11:30:00+00:00, Price: 6.7861
NO OUTLIERS
Upper Outliers for ARCLK in 2018/6
Timestamp: 2018-06-01 06:45:00+00:00, Price: 16.62
Timestamp: 2018-06-01 07:00:00+00:00, Price: 16.42
Timestamp: 2018-06-01 09:15:00+00:00, Price: 16.44
Timestamp: 2018-06-01 09:30:00+00:00, Price: 16.45
Timestamp: 2018-06-01 09:45:00+00:00, Price: 16.45
Timestamp: 2018-06-01 10:45:00+00:00, Price: 16.55
Timestamp: 2018-06-01 11:00:00+00:00, Price: 16.5
Timestamp: 2018-06-01 11:30:00+00:00, Price: 16.42
Timestamp: 2018-06-01 12:30:00+00:00, Price: 16.42
Timestamp: 2018-06-01 12:45:00+00:00, Price: 16.48
Timestamp: 2018-06-01 13:00:00+00:00, Price: 16.48
Timestamp: 2018-06-01 13:30:00+00:00, Price: 16.46
Timestamp: 2018-06-01 13:45:00+00:00, Price: 16.49
Timestamp: 2018-06-01 14:00:00+00:00, Price: 16.46
Timestamp: 2018-06-04 07:00:00+00:00, Price: 16.5
Timestamp: 2018-06-04 07:15:00+00:00, Price: 16.46
Timestamp: 2018-06-04 07:30:00+00:00, Price: 16.5
Timestamp: 2018-06-04 07:45:00+00:00, Price: 16.53
Timestamp: 2018-06-04 08:00:00+00:00, Price: 16.51
Timestamp: 2018-06-04 08:15:00+00:00, Price: 16.48
Timestamp: 2018-06-04 08:30:00+00:00, Price: 16.51
Timestamp: 2018-06-04 08:45:00+00:00, Price: 16.53
Timestamp: 2018-06-04 09:00:00+00:00, Price: 16.54
Timestamp: 2018-06-04 09:15:00+00:00, Price: 16.54
Timestamp: 2018-06-04 09:30:00+00:00, Price: 16.51
Timestamp: 2018-06-04 09:45:00+00:00, Price: 16.43
Timestamp: 2018-06-04 10:00:00+00:00, Price: 16.43
Timestamp: 2018-06-04 10:45:00+00:00, Price: 16.43
Timestamp: 2018-06-04 11:30:00+00:00, Price: 16.44
Timestamp: 2018-06-04 11:45:00+00:00, Price: 16.43
Timestamp: 2018-06-22 06:45:00+00:00, Price: 16.5
Timestamp: 2018-06-25 06:45:00+00:00, Price: 16.5
Timestamp: 2018-06-25 07:00:00+00:00, Price: 16.5
NO OUTLIERS
Upper Outliers for TCELL in 2018/6
Timestamp: 2018-06-28 09:15:00+00:00, Price: 11.6377
Timestamp: 2018-06-28 09:45:00+00:00, Price: 11.6471
Timestamp: 2018-06-28 10:45:00+00:00, Price: 11.6567
Timestamp: 2018-06-28 11:00:00+00:00, Price: 11.6662
Timestamp: 2018-06-28 11:15:00+00:00, Price: 11.6377
Timestamp: 2018-06-28 13:00:00+00:00, Price: 11.7138
Timestamp: 2018-06-28 13:15:00+00:00, Price: 11.7613
Timestamp: 2018-06-28 13:30:00+00:00, Price: 11.7518
Timestamp: 2018-06-28 13:45:00+00:00, Price: 11.7233
Timestamp: 2018-06-28 14:00:00+00:00, Price: 11.7613
Timestamp: 2018-06-28 14:15:00+00:00, Price: 11.7233
Timestamp: 2018-06-28 14:30:00+00:00, Price: 11.7518
Timestamp: 2018-06-28 14:45:00+00:00, Price: 11.6947
Timestamp: 2018-06-28 15:00:00+00:00, Price: 11.7138
Timestamp: 2018-06-29 06:45:00+00:00, Price: 11.7994
Timestamp: 2018-06-29 07:00:00+00:00, Price: 11.9136
Timestamp: 2018-06-29 07:15:00+00:00, Price: 11.9041
Timestamp: 2018-06-29 07:30:00+00:00, Price: 11.8851
Timestamp: 2018-06-29 07:45:00+00:00, Price: 11.8565
Timestamp: 2018-06-29 08:00:00+00:00, Price: 11.8089
Timestamp: 2018-06-29 08:15:00+00:00, Price: 11.7994
Timestamp: 2018-06-29 08:30:00+00:00, Price: 11.7709
Timestamp: 2018-06-29 08:45:00+00:00, Price: 11.7613
Timestamp: 2018-06-29 09:00:00+00:00, Price: 11.7518
Timestamp: 2018-06-29 09:15:00+00:00, Price: 11.7994
Timestamp: 2018-06-29 09:30:00+00:00, Price: 11.7994
Timestamp: 2018-06-29 09:45:00+00:00, Price: 11.7899
Timestamp: 2018-06-29 10:45:00+00:00, Price: 11.8185
Timestamp: 2018-06-29 11:00:00+00:00, Price: 11.8279
Timestamp: 2018-06-29 11:15:00+00:00, Price: 11.8185
Timestamp: 2018-06-29 11:30:00+00:00, Price: 11.8851
Timestamp: 2018-06-29 11:45:00+00:00, Price: 11.8755
Timestamp: 2018-06-29 12:00:00+00:00, Price: 11.8945
Timestamp: 2018-06-29 12:15:00+00:00, Price: 11.8565
Timestamp: 2018-06-29 12:30:00+00:00, Price: 11.8375
Timestamp: 2018-06-29 12:45:00+00:00, Price: 11.7709
Timestamp: 2018-06-29 13:00:00+00:00, Price: 11.7709
Timestamp: 2018-06-29 13:15:00+00:00, Price: 11.7518
Timestamp: 2018-06-29 13:30:00+00:00, Price: 11.7043
Timestamp: 2018-06-29 13:45:00+00:00, Price: 11.6757
Timestamp: 2018-06-29 14:00:00+00:00, Price: 11.6377
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Lower Outliers for THYAO in 2018/8
Timestamp: 2018-08-17 07:00:00+00:00, Price: 15.01
Timestamp: 2018-08-17 07:15:00+00:00, Price: 14.65
Timestamp: 2018-08-17 07:30:00+00:00, Price: 14.35
Timestamp: 2018-08-17 07:45:00+00:00, Price: 14.48
Timestamp: 2018-08-17 08:00:00+00:00, Price: 14.72
Timestamp: 2018-08-17 08:15:00+00:00, Price: 14.67
Timestamp: 2018-08-17 08:30:00+00:00, Price: 14.62
Timestamp: 2018-08-17 08:45:00+00:00, Price: 14.57
Timestamp: 2018-08-17 09:00:00+00:00, Price: 14.6
Timestamp: 2018-08-17 09:15:00+00:00, Price: 14.52
Timestamp: 2018-08-17 09:30:00+00:00, Price: 14.58
Timestamp: 2018-08-17 09:45:00+00:00, Price: 14.57
Timestamp: 2018-08-17 10:00:00+00:00, Price: 14.58
Timestamp: 2018-08-17 10:45:00+00:00, Price: 14.59
Timestamp: 2018-08-17 11:00:00+00:00, Price: 14.91
Timestamp: 2018-08-17 11:15:00+00:00, Price: 15.11
Timestamp: 2018-08-17 11:30:00+00:00, Price: 15.09
Timestamp: 2018-08-17 12:00:00+00:00, Price: 15.07
Timestamp: 2018-08-17 12:15:00+00:00, Price: 15.0
Timestamp: 2018-08-17 12:30:00+00:00, Price: 15.06
Timestamp: 2018-08-17 12:45:00+00:00, Price: 15.01
Timestamp: 2018-08-17 13:00:00+00:00, Price: 14.97
Timestamp: 2018-08-17 13:15:00+00:00, Price: 14.96
Timestamp: 2018-08-17 13:30:00+00:00, Price: 15.09
Timestamp: 2018-08-17 14:15:00+00:00, Price: 15.11
Timestamp: 2018-08-17 14:30:00+00:00, Price: 15.09
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Lower Outliers for TUPRS in 2018/10
Timestamp: 2018-10-30 11:00:00+00:00, Price: 109.9687
Timestamp: 2018-10-30 11:15:00+00:00, Price: 109.3429
Timestamp: 2018-10-30 11:30:00+00:00, Price: 109.5217
Timestamp: 2018-10-30 11:45:00+00:00, Price: 109.6111
Timestamp: 2018-10-30 12:00:00+00:00, Price: 109.5217
Timestamp: 2018-10-30 12:15:00+00:00, Price: 109.5217
Timestamp: 2018-10-30 12:30:00+00:00, Price: 109.8793
Timestamp: 2018-10-30 12:45:00+00:00, Price: 109.6111
Timestamp: 2018-10-30 13:00:00+00:00, Price: 109.7005
Timestamp: 2018-10-30 13:15:00+00:00, Price: 109.9687
Timestamp: 2018-10-30 13:30:00+00:00, Price: 109.7005
Timestamp: 2018-10-30 13:45:00+00:00, Price: 110.1475
NO OUTLIERS
NO OUTLIERS
Lower Outliers for AKBNK in 2018/11
Timestamp: 2018-11-01 06:45:00+00:00, Price: 5.6451
Timestamp: 2018-11-01 07:00:00+00:00, Price: 5.6365
Timestamp: 2018-11-01 07:15:00+00:00, Price: 5.5679
Timestamp: 2018-11-01 07:30:00+00:00, Price: 5.5507
Timestamp: 2018-11-01 07:45:00+00:00, Price: 5.5679
Timestamp: 2018-11-01 08:00:00+00:00, Price: 5.6193
Timestamp: 2018-11-01 08:15:00+00:00, Price: 5.6365
Timestamp: 2018-11-01 08:30:00+00:00, Price: 5.6536
Timestamp: 2018-11-01 08:45:00+00:00, Price: 5.7309
Timestamp: 2018-11-01 09:00:00+00:00, Price: 5.7223
Timestamp: 2018-11-01 09:15:00+00:00, Price: 5.7309
Timestamp: 2018-11-01 09:30:00+00:00, Price: 5.7394
Timestamp: 2018-11-01 09:45:00+00:00, Price: 5.7823
Timestamp: 2018-11-01 10:00:00+00:00, Price: 5.7823
Timestamp: 2018-11-01 10:45:00+00:00, Price: 5.7823
Timestamp: 2018-11-01 11:00:00+00:00, Price: 5.7652
Timestamp: 2018-11-01 11:15:00+00:00, Price: 5.7394
Timestamp: 2018-11-01 11:30:00+00:00, Price: 5.7652
Timestamp: 2018-11-01 11:45:00+00:00, Price: 5.7909
Timestamp: 2018-11-01 12:00:00+00:00, Price: 5.7995
Timestamp: 2018-11-01 12:15:00+00:00, Price: 5.7823
Timestamp: 2018-11-01 12:30:00+00:00, Price: 5.7309
Timestamp: 2018-11-01 12:45:00+00:00, Price: 5.7566
Timestamp: 2018-11-01 13:00:00+00:00, Price: 5.8252
Timestamp: 2018-11-01 13:15:00+00:00, Price: 5.8252
Timestamp: 2018-11-01 13:30:00+00:00, Price: 5.8081
Timestamp: 2018-11-01 13:45:00+00:00, Price: 5.8081
Timestamp: 2018-11-01 14:00:00+00:00, Price: 5.8424
Upper Outliers for VAKBN in 2018/11
Timestamp: 2018-11-30 14:45:00+00:00, Price: 4.03
Lower Outliers for VAKBN in 2018/11
Timestamp: 2018-11-01 07:00:00+00:00, Price: 3.42
Timestamp: 2018-11-01 07:30:00+00:00, Price: 3.42
Timestamp: 2018-11-01 07:45:00+00:00, Price: 3.42
Timestamp: 2018-11-01 08:00:00+00:00, Price: 3.42
NO OUTLIERS
Upper Outliers for TUPRS in 2018/11
Timestamp: 2018-11-05 11:15:00+00:00, Price: 122.3067
Timestamp: 2018-11-05 11:45:00+00:00, Price: 122.6643
Timestamp: 2018-11-05 12:00:00+00:00, Price: 122.3961
Timestamp: 2018-11-05 12:30:00+00:00, Price: 122.4855
Timestamp: 2018-11-05 12:45:00+00:00, Price: 122.4855
Timestamp: 2018-11-05 13:00:00+00:00, Price: 122.3067
Timestamp: 2018-11-05 13:15:00+00:00, Price: 122.3067
Timestamp: 2018-11-05 13:30:00+00:00, Price: 123.1113
Timestamp: 2018-11-05 14:30:00+00:00, Price: 122.4855
Timestamp: 2018-11-05 14:45:00+00:00, Price: 122.5749
Timestamp: 2018-11-05 15:00:00+00:00, Price: 123.2901
Timestamp: 2018-11-06 06:45:00+00:00, Price: 123.2901
Timestamp: 2018-11-06 07:00:00+00:00, Price: 122.3067
Timestamp: 2018-11-06 08:30:00+00:00, Price: 122.3961
Timestamp: 2018-11-07 11:30:00+00:00, Price: 122.3067
Timestamp: 2018-11-07 12:15:00+00:00, Price: 123.1113
Timestamp: 2018-11-07 12:30:00+00:00, Price: 123.3795
Timestamp: 2018-11-07 12:45:00+00:00, Price: 123.5583
Timestamp: 2018-11-07 13:00:00+00:00, Price: 123.5583
Timestamp: 2018-11-07 13:15:00+00:00, Price: 123.3795
Timestamp: 2018-11-07 13:30:00+00:00, Price: 123.3795
Timestamp: 2018-11-07 13:45:00+00:00, Price: 123.3795
Timestamp: 2018-11-07 14:00:00+00:00, Price: 123.6477
Timestamp: 2018-11-07 14:15:00+00:00, Price: 123.5583
Timestamp: 2018-11-07 14:30:00+00:00, Price: 123.7371
Timestamp: 2018-11-07 14:45:00+00:00, Price: 123.2901
Timestamp: 2018-11-07 15:00:00+00:00, Price: 122.9325
Upper Outliers for TCELL in 2018/11
Timestamp: 2018-11-28 12:45:00+00:00, Price: 12.6381
Timestamp: 2018-11-28 13:00:00+00:00, Price: 12.6869
Timestamp: 2018-11-28 13:15:00+00:00, Price: 12.7064
Timestamp: 2018-11-28 13:30:00+00:00, Price: 12.6967
Timestamp: 2018-11-28 13:45:00+00:00, Price: 12.6576
Timestamp: 2018-11-29 07:15:00+00:00, Price: 12.7064
Timestamp: 2018-11-29 07:30:00+00:00, Price: 12.6967
Timestamp: 2018-11-29 07:45:00+00:00, Price: 12.6869
Timestamp: 2018-11-29 08:00:00+00:00, Price: 12.6381
Timestamp: 2018-11-29 09:15:00+00:00, Price: 12.6674
Timestamp: 2018-11-29 09:30:00+00:00, Price: 12.6478
Timestamp: 2018-11-29 11:00:00+00:00, Price: 12.6381
Timestamp: 2018-11-29 11:15:00+00:00, Price: 12.6478
Timestamp: 2018-11-29 11:30:00+00:00, Price: 12.6478
Timestamp: 2018-11-29 11:45:00+00:00, Price: 12.6381
Timestamp: 2018-11-29 12:00:00+00:00, Price: 12.6381
Timestamp: 2018-11-29 12:15:00+00:00, Price: 12.6478
Timestamp: 2018-11-29 12:30:00+00:00, Price: 12.6576
Timestamp: 2018-11-29 12:45:00+00:00, Price: 12.6576
NO OUTLIERS
Upper Outliers for AKBNK in 2018/12
Timestamp: 2018-12-03 06:45:00+00:00, Price: 6.6831
Timestamp: 2018-12-03 07:00:00+00:00, Price: 6.7003
Timestamp: 2018-12-03 07:15:00+00:00, Price: 6.6831
Timestamp: 2018-12-03 07:30:00+00:00, Price: 6.6917
Timestamp: 2018-12-03 07:45:00+00:00, Price: 6.6746
Timestamp: 2018-12-03 08:00:00+00:00, Price: 6.7089
Timestamp: 2018-12-03 08:15:00+00:00, Price: 6.7175
Timestamp: 2018-12-03 08:30:00+00:00, Price: 6.6746
Timestamp: 2018-12-03 08:45:00+00:00, Price: 6.6574
Timestamp: 2018-12-03 09:00:00+00:00, Price: 6.6402
Timestamp: 2018-12-03 09:15:00+00:00, Price: 6.6488
Timestamp: 2018-12-03 09:30:00+00:00, Price: 6.6574
Timestamp: 2018-12-03 09:45:00+00:00, Price: 6.6488
Timestamp: 2018-12-03 10:45:00+00:00, Price: 6.6317
Timestamp: 2018-12-03 11:00:00+00:00, Price: 6.5973
Timestamp: 2018-12-03 11:15:00+00:00, Price: 6.5802
Timestamp: 2018-12-03 11:30:00+00:00, Price: 6.563
Timestamp: 2018-12-03 11:45:00+00:00, Price: 6.5545
Timestamp: 2018-12-03 12:00:00+00:00, Price: 6.5373
Timestamp: 2018-12-03 12:15:00+00:00, Price: 6.563
Timestamp: 2018-12-03 12:30:00+00:00, Price: 6.5545
Timestamp: 2018-12-03 12:45:00+00:00, Price: 6.5201
Timestamp: 2018-12-03 13:00:00+00:00, Price: 6.5459
Timestamp: 2018-12-03 13:15:00+00:00, Price: 6.5716
Timestamp: 2018-12-03 13:30:00+00:00, Price: 6.563
Timestamp: 2018-12-03 13:45:00+00:00, Price: 6.5459
Timestamp: 2018-12-03 14:00:00+00:00, Price: 6.5716
Timestamp: 2018-12-03 14:15:00+00:00, Price: 6.5287
Timestamp: 2018-12-03 14:30:00+00:00, Price: 6.503
Timestamp: 2018-12-03 14:45:00+00:00, Price: 6.503
Timestamp: 2018-12-03 15:00:00+00:00, Price: 6.4944
Timestamp: 2018-12-04 06:45:00+00:00, Price: 6.4687
Timestamp: 2018-12-04 07:00:00+00:00, Price: 6.5116
Timestamp: 2018-12-04 07:15:00+00:00, Price: 6.5116
Timestamp: 2018-12-04 07:30:00+00:00, Price: 6.4944
Timestamp: 2018-12-04 07:45:00+00:00, Price: 6.4944
Timestamp: 2018-12-04 08:00:00+00:00, Price: 6.4601
Timestamp: 2018-12-04 08:15:00+00:00, Price: 6.4515
Timestamp: 2018-12-04 08:30:00+00:00, Price: 6.4515
Timestamp: 2018-12-04 08:45:00+00:00, Price: 6.4515
Timestamp: 2018-12-04 09:00:00+00:00, Price: 6.4515
Timestamp: 2018-12-04 09:15:00+00:00, Price: 6.4429
Timestamp: 2018-12-04 09:30:00+00:00, Price: 6.4515
Timestamp: 2018-12-04 09:45:00+00:00, Price: 6.4687
Timestamp: 2018-12-04 10:00:00+00:00, Price: 6.4643999999999995
Timestamp: 2018-12-04 10:45:00+00:00, Price: 6.4601
Timestamp: 2018-12-04 11:00:00+00:00, Price: 6.4601
Timestamp: 2018-12-04 11:15:00+00:00, Price: 6.4515
Timestamp: 2018-12-04 11:30:00+00:00, Price: 6.4515
Timestamp: 2018-12-04 11:45:00+00:00, Price: 6.4601
Timestamp: 2018-12-04 12:00:00+00:00, Price: 6.4601
Timestamp: 2018-12-04 12:15:00+00:00, Price: 6.4429
Timestamp: 2018-12-04 12:30:00+00:00, Price: 6.4343
Timestamp: 2018-12-04 12:45:00+00:00, Price: 6.4258
Timestamp: 2018-12-04 13:00:00+00:00, Price: 6.4086
Timestamp: 2018-12-04 13:15:00+00:00, Price: 6.4086
Timestamp: 2018-12-04 13:30:00+00:00, Price: 6.3914
Timestamp: 2018-12-04 13:45:00+00:00, Price: 6.34
Timestamp: 2018-12-04 14:00:00+00:00, Price: 6.3228
Timestamp: 2018-12-04 14:15:00+00:00, Price: 6.3057
Timestamp: 2018-12-04 14:30:00+00:00, Price: 6.3228
Timestamp: 2018-12-04 14:45:00+00:00, Price: 6.2799
Timestamp: 2018-12-04 15:00:00+00:00, Price: 6.2885
Lower Outliers for AKBNK in 2018/12
Timestamp: 2018-12-14 08:45:00+00:00, Price: 5.4306
Timestamp: 2018-12-14 09:00:00+00:00, Price: 5.422
Timestamp: 2018-12-14 09:15:00+00:00, Price: 5.4306
Timestamp: 2018-12-14 10:45:00+00:00, Price: 5.4306
Timestamp: 2018-12-14 11:00:00+00:00, Price: 5.4048
Timestamp: 2018-12-14 11:30:00+00:00, Price: 5.4306
Timestamp: 2018-12-14 11:45:00+00:00, Price: 5.4306
Timestamp: 2018-12-14 12:15:00+00:00, Price: 5.422
Timestamp: 2018-12-14 12:30:00+00:00, Price: 5.4306
Timestamp: 2018-12-14 12:45:00+00:00, Price: 5.4134
Timestamp: 2018-12-14 13:00:00+00:00, Price: 5.4134
Timestamp: 2018-12-14 13:15:00+00:00, Price: 5.3963
Timestamp: 2018-12-14 13:30:00+00:00, Price: 5.4048
Timestamp: 2018-12-14 13:45:00+00:00, Price: 5.422
Timestamp: 2018-12-14 14:00:00+00:00, Price: 5.422
Timestamp: 2018-12-14 14:15:00+00:00, Price: 5.4306
Timestamp: 2018-12-14 14:30:00+00:00, Price: 5.4306
Timestamp: 2018-12-14 14:45:00+00:00, Price: 5.422
Upper Outliers for VAKBN in 2018/12
Timestamp: 2018-12-03 06:45:00+00:00, Price: 4.1
Timestamp: 2018-12-03 07:00:00+00:00, Price: 4.06
Timestamp: 2018-12-03 07:15:00+00:00, Price: 4.08
Timestamp: 2018-12-03 07:30:00+00:00, Price: 4.11
Timestamp: 2018-12-03 07:45:00+00:00, Price: 4.1
Timestamp: 2018-12-03 08:00:00+00:00, Price: 4.1
Timestamp: 2018-12-03 08:15:00+00:00, Price: 4.09
Timestamp: 2018-12-03 08:30:00+00:00, Price: 4.08
Timestamp: 2018-12-03 08:45:00+00:00, Price: 4.07
Timestamp: 2018-12-03 09:00:00+00:00, Price: 4.07
Timestamp: 2018-12-03 09:15:00+00:00, Price: 4.08
Timestamp: 2018-12-03 09:30:00+00:00, Price: 4.09
Timestamp: 2018-12-03 09:45:00+00:00, Price: 4.08
Timestamp: 2018-12-03 10:45:00+00:00, Price: 4.07
Timestamp: 2018-12-03 11:00:00+00:00, Price: 4.07
Timestamp: 2018-12-03 11:15:00+00:00, Price: 4.07
Timestamp: 2018-12-03 11:30:00+00:00, Price: 4.06
Timestamp: 2018-12-03 11:45:00+00:00, Price: 4.06
Timestamp: 2018-12-03 12:15:00+00:00, Price: 4.07
Timestamp: 2018-12-03 13:00:00+00:00, Price: 4.06
Timestamp: 2018-12-03 13:15:00+00:00, Price: 4.06
Timestamp: 2018-12-03 13:30:00+00:00, Price: 4.09
Timestamp: 2018-12-03 13:45:00+00:00, Price: 4.07
Timestamp: 2018-12-03 14:00:00+00:00, Price: 4.08
Timestamp: 2018-12-03 14:15:00+00:00, Price: 4.06
Lower Outliers for VAKBN in 2018/12
Timestamp: 2018-12-17 15:00:00+00:00, Price: 3.69
Timestamp: 2018-12-18 06:45:00+00:00, Price: 3.67
Upper Outliers for ARCLK in 2018/12
Timestamp: 2018-12-03 08:15:00+00:00, Price: 15.9
Lower Outliers for ARCLK in 2018/12
Timestamp: 2018-12-12 13:15:00+00:00, Price: 15.1
NO OUTLIERS
NO OUTLIERS
Upper Outliers for THYAO in 2018/12
Timestamp: 2018-12-03 06:45:00+00:00, Price: 17.11
Timestamp: 2018-12-03 07:00:00+00:00, Price: 17.06
Timestamp: 2018-12-03 07:15:00+00:00, Price: 17.07
Timestamp: 2018-12-03 07:30:00+00:00, Price: 17.12
Timestamp: 2018-12-03 07:45:00+00:00, Price: 17.1
Timestamp: 2018-12-03 08:00:00+00:00, Price: 17.08
Timestamp: 2018-12-03 08:15:00+00:00, Price: 17.03
Lower Outliers for THYAO in 2018/12
Timestamp: 2018-12-14 08:15:00+00:00, Price: 14.93
Timestamp: 2018-12-14 08:30:00+00:00, Price: 14.89
Timestamp: 2018-12-14 08:45:00+00:00, Price: 14.88
Timestamp: 2018-12-14 09:00:00+00:00, Price: 14.9
Timestamp: 2018-12-14 09:15:00+00:00, Price: 14.91
Timestamp: 2018-12-14 09:30:00+00:00, Price: 14.84
Timestamp: 2018-12-14 09:45:00+00:00, Price: 14.82
Timestamp: 2018-12-14 10:00:00+00:00, Price: 14.815000000000001
Timestamp: 2018-12-14 10:45:00+00:00, Price: 14.81
Timestamp: 2018-12-14 11:00:00+00:00, Price: 14.79
Timestamp: 2018-12-14 11:15:00+00:00, Price: 14.85
Timestamp: 2018-12-14 11:30:00+00:00, Price: 14.82
Timestamp: 2018-12-14 11:45:00+00:00, Price: 14.85
Timestamp: 2018-12-14 12:00:00+00:00, Price: 14.84
Timestamp: 2018-12-14 12:15:00+00:00, Price: 14.89
Timestamp: 2018-12-14 12:30:00+00:00, Price: 14.9
Timestamp: 2018-12-14 12:45:00+00:00, Price: 14.84
Timestamp: 2018-12-14 13:00:00+00:00, Price: 14.83
Timestamp: 2018-12-14 13:15:00+00:00, Price: 14.78
Timestamp: 2018-12-14 13:30:00+00:00, Price: 14.82
Timestamp: 2018-12-14 13:45:00+00:00, Price: 14.86
Timestamp: 2018-12-14 14:00:00+00:00, Price: 14.88
Timestamp: 2018-12-14 14:15:00+00:00, Price: 14.89
Timestamp: 2018-12-14 14:30:00+00:00, Price: 14.89
Timestamp: 2018-12-14 14:45:00+00:00, Price: 14.9
Timestamp: 2018-12-14 15:00:00+00:00, Price: 14.96
Timestamp: 2018-12-17 06:45:00+00:00, Price: 15.03
Timestamp: 2018-12-17 07:00:00+00:00, Price: 14.98
Timestamp: 2018-12-17 07:15:00+00:00, Price: 14.92
Timestamp: 2018-12-17 07:30:00+00:00, Price: 14.9
Timestamp: 2018-12-17 07:45:00+00:00, Price: 14.94
Timestamp: 2018-12-17 08:00:00+00:00, Price: 14.93
Timestamp: 2018-12-17 08:15:00+00:00, Price: 15.01
Timestamp: 2018-12-17 08:30:00+00:00, Price: 15.0
Timestamp: 2018-12-17 08:45:00+00:00, Price: 15.02
Timestamp: 2018-12-17 09:00:00+00:00, Price: 14.98
Timestamp: 2018-12-17 09:15:00+00:00, Price: 15.03
Timestamp: 2018-12-17 09:30:00+00:00, Price: 15.05
Timestamp: 2018-12-17 09:45:00+00:00, Price: 15.0
Timestamp: 2018-12-17 10:00:00+00:00, Price: 15.004999999999999
Timestamp: 2018-12-17 10:45:00+00:00, Price: 15.01
Timestamp: 2018-12-17 11:00:00+00:00, Price: 15.01
Timestamp: 2018-12-17 11:15:00+00:00, Price: 14.96
Timestamp: 2018-12-17 11:30:00+00:00, Price: 14.85
Timestamp: 2018-12-17 11:45:00+00:00, Price: 14.9
Timestamp: 2018-12-17 12:00:00+00:00, Price: 14.9
Timestamp: 2018-12-17 12:15:00+00:00, Price: 14.91
Timestamp: 2018-12-17 12:30:00+00:00, Price: 14.9
Timestamp: 2018-12-17 12:45:00+00:00, Price: 14.86
Timestamp: 2018-12-17 13:00:00+00:00, Price: 14.83
Timestamp: 2018-12-17 13:15:00+00:00, Price: 14.86
Timestamp: 2018-12-17 13:30:00+00:00, Price: 14.84
Timestamp: 2018-12-17 13:45:00+00:00, Price: 14.82
Timestamp: 2018-12-17 14:00:00+00:00, Price: 14.84
Timestamp: 2018-12-17 14:15:00+00:00, Price: 14.84
Timestamp: 2018-12-17 14:30:00+00:00, Price: 14.74
Timestamp: 2018-12-17 14:45:00+00:00, Price: 14.75
Timestamp: 2018-12-17 15:00:00+00:00, Price: 14.69
Timestamp: 2018-12-18 06:45:00+00:00, Price: 14.69
Timestamp: 2018-12-18 07:00:00+00:00, Price: 14.79
Timestamp: 2018-12-18 07:15:00+00:00, Price: 14.83
Timestamp: 2018-12-18 07:30:00+00:00, Price: 14.85
Timestamp: 2018-12-18 07:45:00+00:00, Price: 14.83
Timestamp: 2018-12-18 08:00:00+00:00, Price: 14.83
Timestamp: 2018-12-18 08:15:00+00:00, Price: 14.83
Timestamp: 2018-12-18 08:30:00+00:00, Price: 14.82
Timestamp: 2018-12-18 08:45:00+00:00, Price: 14.83
Timestamp: 2018-12-18 09:00:00+00:00, Price: 14.84
Timestamp: 2018-12-18 09:15:00+00:00, Price: 14.84
Timestamp: 2018-12-18 09:30:00+00:00, Price: 14.87
Timestamp: 2018-12-18 09:45:00+00:00, Price: 15.0
Timestamp: 2018-12-18 10:00:00+00:00, Price: 15.0
Timestamp: 2018-12-18 10:45:00+00:00, Price: 15.02
Timestamp: 2018-12-18 11:00:00+00:00, Price: 15.01
Timestamp: 2018-12-18 11:15:00+00:00, Price: 14.99
Timestamp: 2018-12-18 11:30:00+00:00, Price: 15.03
Timestamp: 2018-12-18 11:45:00+00:00, Price: 15.03
Timestamp: 2018-12-18 12:00:00+00:00, Price: 14.96
Timestamp: 2018-12-18 12:15:00+00:00, Price: 14.97
Timestamp: 2018-12-18 12:30:00+00:00, Price: 15.08
Timestamp: 2018-12-18 12:45:00+00:00, Price: 15.08
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Upper Outliers for THYAO in 2019/1
Timestamp: 2019-01-02 06:45:00+00:00, Price: 16.13
Timestamp: 2019-01-02 07:00:00+00:00, Price: 16.15
Timestamp: 2019-01-02 07:15:00+00:00, Price: 16.17
Timestamp: 2019-01-02 07:30:00+00:00, Price: 16.23
Timestamp: 2019-01-02 07:45:00+00:00, Price: 16.24
Timestamp: 2019-01-02 08:00:00+00:00, Price: 16.15
Timestamp: 2019-01-02 08:15:00+00:00, Price: 16.1
Timestamp: 2019-01-02 08:30:00+00:00, Price: 16.14
Timestamp: 2019-01-02 08:45:00+00:00, Price: 16.12
Timestamp: 2019-01-02 09:00:00+00:00, Price: 16.15
Timestamp: 2019-01-02 09:15:00+00:00, Price: 16.17
Timestamp: 2019-01-02 09:30:00+00:00, Price: 16.04
Timestamp: 2019-01-02 09:45:00+00:00, Price: 16.03
Timestamp: 2019-01-02 10:00:00+00:00, Price: 16.025
Timestamp: 2019-01-02 10:45:00+00:00, Price: 16.02
Timestamp: 2019-01-02 11:00:00+00:00, Price: 15.97
Timestamp: 2019-01-02 11:15:00+00:00, Price: 15.96
Timestamp: 2019-01-02 11:30:00+00:00, Price: 15.93
Timestamp: 2019-01-02 11:45:00+00:00, Price: 15.95
Timestamp: 2019-01-02 12:00:00+00:00, Price: 15.98
Timestamp: 2019-01-02 12:15:00+00:00, Price: 15.95
Timestamp: 2019-01-02 12:30:00+00:00, Price: 15.95
Timestamp: 2019-01-02 12:45:00+00:00, Price: 15.96
Timestamp: 2019-01-02 13:00:00+00:00, Price: 15.97
Timestamp: 2019-01-02 13:15:00+00:00, Price: 15.86
Timestamp: 2019-01-02 13:30:00+00:00, Price: 15.73
Timestamp: 2019-01-02 13:45:00+00:00, Price: 15.77
Timestamp: 2019-01-02 14:00:00+00:00, Price: 15.75
Timestamp: 2019-01-02 14:30:00+00:00, Price: 15.7
Timestamp: 2019-01-02 14:45:00+00:00, Price: 15.68
Timestamp: 2019-01-02 15:00:00+00:00, Price: 15.72
Timestamp: 2019-01-03 07:00:00+00:00, Price: 15.76
Timestamp: 2019-01-03 07:15:00+00:00, Price: 15.79
Timestamp: 2019-01-03 07:30:00+00:00, Price: 15.74
Timestamp: 2019-01-03 07:45:00+00:00, Price: 15.68
Timestamp: 2019-01-03 08:00:00+00:00, Price: 15.77
Timestamp: 2019-01-03 08:15:00+00:00, Price: 15.83
Timestamp: 2019-01-03 08:30:00+00:00, Price: 15.95
Timestamp: 2019-01-03 08:45:00+00:00, Price: 15.85
Timestamp: 2019-01-03 09:00:00+00:00, Price: 15.93
Timestamp: 2019-01-03 09:15:00+00:00, Price: 15.89
Timestamp: 2019-01-03 09:30:00+00:00, Price: 15.82
Timestamp: 2019-01-03 09:45:00+00:00, Price: 15.79
Timestamp: 2019-01-03 10:00:00+00:00, Price: 15.8
Timestamp: 2019-01-03 10:45:00+00:00, Price: 15.81
Timestamp: 2019-01-03 11:00:00+00:00, Price: 15.79
Timestamp: 2019-01-03 11:15:00+00:00, Price: 15.81
Timestamp: 2019-01-03 11:30:00+00:00, Price: 15.73
Timestamp: 2019-01-03 11:45:00+00:00, Price: 15.74
Timestamp: 2019-01-03 12:00:00+00:00, Price: 15.71
Timestamp: 2019-01-03 12:15:00+00:00, Price: 15.7
Timestamp: 2019-01-03 12:30:00+00:00, Price: 15.71
Timestamp: 2019-01-03 13:15:00+00:00, Price: 15.68
Out[7]:

4. Google Trends¶

In this part we will use the data we obtained from Google Trends for the stocks that we are interested in and make a line of search volumes and stock prices to identify the causes.

In [8]:
# returns outliers obtained from control charts in part 3
def get_outliers(stock_name):
    if stock_name == "AKBNK":
        return outliers_akbnk
    elif stock_name == "VAKBN":
        return outliers_vakbn
    elif stock_name == "ARCLK":
        return outliers_arclk
    elif stock_name == "TUPRS":
        return outliers_tuprs
    elif stock_name == "TCELL":
        return outliers_tcell
    else:
        return outliers_thyao

'''
Below function plots the quarterly trend, stock prices, and outliers 
given the stock name.
'''
def plot_trend(stock_name):
    df_trend = pd.read_csv(f"{stock_name}.csv", header=0)
    df_trend["Week"] = pd.to_datetime(df_trend["Week"])
    
    # start and end dates
    # notice that we observe them quarterly
    # but the outlier data we used is obtained from part 3, therefore monthly
    start = pd.to_datetime("2017-01-15")
    end = start + pd.DateOffset(months=3)
    
    # initialize the figure
    fig, axes = plt.subplots(4, 2, figsize=(22, 22))
    fig.subplots_adjust(hspace=0.5)
    
    # we have 8 quarters (2 years)
    for i in range(8):
        # get the outliers for quarter
        outliers = get_outliers(stock_name)
        outliers_quarter = [i for i in outliers if i[0] >= start.tz_localize('UTC') and i[0] <= end.tz_localize('UTC')]
        outliers_quarter = pd.DataFrame(outliers_quarter, columns=['timestamp', 'value'])
        # copy df
        df = filled_df.copy()
        # filtering with time and adding UTC timezone
        ndf = df[(df['timestamp'] >= start.tz_localize('UTC')) & (df['timestamp'] <= end.tz_localize('UTC'))]
        # filtering the necessary columns 
        df = ndf[['timestamp', 'year', 'month', 'day', stock_name]].copy()
        
        # We must scale both of the df between 0-1 since their scale is very different
        scaler = MinMaxScaler(feature_range=(0, 1))
        df[stock_name] = scaler.fit_transform(df[stock_name].values.reshape(-1, 1))
        # scale outliers
        if len(outliers_quarter) > 0:
            outliers_quarter["value"] = scaler.transform(outliers_quarter["value"].values.reshape(-1, 1))
            
        # filtering df_akbnk with the same timezone
        df_trend_filtered = df_trend[(df_trend['Week'] >= start) & (df_trend['Week'] <= end)]

        # min max scaler 
        scaler = MinMaxScaler(feature_range=(0, 1))
        df_trend_filtered[f"IST:{stock_name}: (Türkiye)"] = scaler.fit_transform(df_trend_filtered[f"IST:{stock_name}: (Türkiye)"].values.reshape(-1, 1))

        row = i // 2
        col = i % 2

        # creating a figure and axis
        ax = axes[row, col]
        # plotting the data from df_akbnk_filtered
        ax.plot(df_trend_filtered["Week"], df_trend_filtered[f"IST:{stock_name}: (Türkiye)"], label="Trend Data", color="green", linewidth=3)

        # plotting the data from df_copy (assuming "timestamp" is your x-axis)
        ax.plot(df["timestamp"], df[stock_name], label="Stock Price", color="orangered")

        # plot outliers as red dots
        if len(outliers_quarter) > 0:
            ax.scatter(outliers_quarter['timestamp'], outliers_quarter['value'], c='red', marker='o', s=100, label='Outliers')

        # x, y axis, title and legend
        ax.set_xlabel(f"Q{i % 4 + 1} {start.year}", fontsize=16)
        ax.set_ylabel("Stock Prices and \nSearch Volume \n(Normalized 0-1)", fontsize=16)
        ax.grid(True)
        ax.set_title(f"{stock_name}\nTrend Data vs. Stock Price", fontsize=18, fontweight="bold")
        ax.legend(loc="upper left")

        # off-setting dates
        start = end
        end = start + relativedelta(months=3)

    plt.show()

4.1 IST:AKBNK¶

In [9]:
plot_trend("AKBNK")

4.2 IST:VAKBN¶

In [10]:
plot_trend("VAKBN")

4.3 IST:ARCLK¶

In [11]:
plot_trend("ARCLK")

4.4 IST:TUPRS¶

In [12]:
plot_trend("TUPRS")

4.5 IST:TCELL¶

In [13]:
plot_trend("TCELL")

4.6 IST:THYAO¶

In [14]:
plot_trend("THYAO")
In [ ]: